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Developer-Y cs-video-courses
List of Computer Science courses with video lectures.
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README.md
View README on GitHub
- Please check NOTES for general information about this list.
- Please refer CONTRIBUTING.md for contribution guidelines.
- Please feel free to raise any genuine issue you may have, however, it has been noticed that few people open empty issues to raise their GitHub contribution on their account. Such spammers will be blocked.
- You are welcome to contribute, please create PR for actual college/University level courses. Please do not add links for small MOOCs, basic tutorials, or advertisements for some sites/channels.
Table of Contents
- Introduction to Computer Science
- Data Structures and Algorithms
- Systems Programming
- Database Systems
- Software Engineering
- Artificial Intelligence
- Machine Learning
- Computer Networks
- Math for Computer Scientist
- Web Programming and Internet Technologies
- Theoretical CS and Programming Languages
- Embedded Systems
- Real time system evaluation
- Computer Organization and Architecture
- Security
- Computer Graphics
- Image Processing and Computer Vision
- Computational Physics
- Computational Biology
- Quantum Computing
- Robotics and Control
- Computational Finance
- Network Science
- Blockchain Development
- Misc
- COMP6991 - Rust Programming - UNSW (2022) | Video Playlist
- COMP1511 - Programming Fundamentals - UNSW (Complete Course: Lectures, Labs, Assignments, Exercises, Exams) | Video Playlist
- CS 10 - The Beauty and Joy of Computing - Spring 2015 - Dan Garcia - UC Berkeley InfoCoBuild
- 6.0001 - Introduction to Computer Science and Programming in Python - MIT OCW
- 6.001 - Structure and Interpretation of Computer Programs, MIT
- Introduction to Computational Thinking - MIT
- CS 50 - Introduction to Computer Science, Harvard University (cs50.tv)
- CS50R - Introduction to Programming with R (Lecture Videos)
- CS50P - CS50's Introduction to Programming with Python - Harvard (David J. Malan) (Lecture Videos)
- CS 61A - Structure and Interpretation of Computer Programs [Python], UC Berkeley
- CPSC 110 - Systematic Program Design [Racket], University of British Columbia
- CS50's Understanding Technology
- CSE 142 Computer Programming I (Java Programming), Spring 2016 - University of Washington
- CS 1301 Intro to computing - Gatech
- CS 106A - Programming Methodology, Stanford University (Lecture Videos)
- CS 106B - Programming Abstractions, Stanford University
- CS 106L - Standard C++ Programming(Lecture Videos)
- CS 106X - Programming Abstractions in C++
- CS 107 - Programming Paradigms, Stanford University
- CmSc 150 - Introduction to Programming with Arcade Games, Simpson College
- IN2377 - Concepts of C++ programming (Winter 2023), TUM (Winter 2022) (Summer 2022) (Summer 2021)
- IN1503 - Advanced C++ Programming, TUM
- LINFO 1104 - Paradigms of computer programming, Peter Van Roy, Université catholique de Louvain, Belgium - EdX
- FP 101x - Introduction to Functional Programming, TU Delft
- Introduction to Problem Solving and Programming - IIT Kanpur
- Introduction to programming in C - IIT Kanpur
- Programming in C++ - IIT Kharagpur
- Python Boot Camp Fall 2016 - Berkeley Institute for Data Science (BIDS)
- CS 101 - Introduction to Computer Science - Udacity
- 6.00SC - Introduction to Computer Science and Programming (Spring 2011) - MIT OCW
- 6.00 - Introduction to Computer Science and Programming (Fall 2008) - MIT OCW
- 6.01SC - Introduction to Electrical Engineering and Computer Science I - MIT OCW
- Modern C++ Course (2018) - Bonn University
- Modern C++ (Lecture & Tutorials, 2020, Vizzo & Stachniss) - University of Bonn
- UW Madison CS 368 C++ for Java Programmers Fall 2020, by Michael Doescher
- UW Madison CS 354 Machine Organization and Programming spring 2020, 2021, by Michael Doescher
- Cornell CS 1110 Introduction to Computing using Python fall 2020, by Walker White (Lecture Videos)
- Cornell ECE 4960 Computational and Software Engineering spring 2017, by Edwin Kan
- ECS 36C - Data Structures and Algorithms (C++) - Spring 2020 - Joël Porquet-Lupine - UC Davis
- Programming and Data Structures with Python, 2021-2022, Sem I - by Prof. Madhavan Mukund, CMI
- Graph Algorithms - Robert Sedgewick - Princeton University
- EECS 477 - Introduction to Algorithms, Winter 2023, UMichigan
- EECS 498 / 598 - Advanced Graph Algorithms: Graph Algorithms via Graph Decomposition, Fall 2025, UMichigan
- EECS 498 / 598 - Advanced Graph Algorithms: Expanders and Fast Graph Algorithms, Fall 2021, UMichigan
- 6.006 - Introduction to Algorithms, MIT OCW
- MIT 6.006 Introduction to Algorithms, Spring 2020
- Algorithms: Design and Analysis 1 - Stanford University
- Algorithms: Design and Analysis 2 - Stanford University
- COS 226 Algorithms, Youtube, Princeton - by Robert Sedgewick and Kevin Wayne
- CSE 331 Introduction to Algorithm Design and Analysis, SUNY University at Buffalo, NY - Fall 2017 (Lectures) (Homework Walkthroughs)
- CSE 373 - Analysis of Algorithms, Stony Brook - Prof Skiena
- COP 3530 Data Structures and Algorithms, Prof Sahni, UFL (Videos)
- CS225 - Data Structures - University of Illinois at Urbana-Champaign(Video lectures)
- CS2 - Data Structures and Algorithms Sim-Mautner - UNSW
- Data Structures - Pepperdine University
- CS 161 - Design and Analysis of Algorithms, Prof. Tim Roughgarden, Stanford University
- 6.046J - Introduction to Algorithms - Fall 2005, MIT OCW
- Introduction to Algorithms (Spring 2020), MIT OCW
- 6.046 - Design and Analysis of Algorithms, Spring 2015 - MIT OCW
- CS 473 - Algorithms - University of Illinois at Urbana-Champaign (Notes - Jeff Erickson) (YouTube)
- COMP300E - Programming Challenges, Prof Skiena, Hong Kong University of Science and Technology - 2009
- 16s-4102 - Algorithms, University of Virginia (Youtube)
- CS 61B - Data Structures (Java) - UC Berkeley(Discussion 2022)
- CS 170 Algorithms - UCBerkeley Fall 2019, Youtube Fall 2018, Youtube Fall 2018,Bilibili 2013 Bilibili
- CS 159 Data-Driven Algorithm Design - Caltech Spring 2020, Youtube
- ECS 122A - Algorithm Design and Analysis, UC Davis
- CSE 373 - Data Structures and Algorithms, Winter 2024 - University of Washington (Winter 2024, Youtube) (Spring 2023, Notes) (Spring 2023, Youtube)
- CSEP 521 - Applied Algorithms, Winter 2013 - University of Washington (Videos)
- Data Structures And Algorithms - IIT Delhi
- Design and Analysis of Algorithms - IIT Bombay
- Programming, Data Structures and Algorithms - IIT Madras
- Design and Analysis of Algorithms - IIT Madras
- Fundamental Algorithms:Design and Analysis - IIT Kharagpur
- Programming and Data Structure - IIT Kharagpur
- Programming, Data structures and Algorithms - IIT Madras
- Programming, Data Structures and Algorithms in Python - IIT Madras
- Programming and Data structures (PDS) - IIT Madras
- COP 5536 Advanced Data Structures, Prof Sahni - UFL (Videos)
- CS 261 - A Second Course in Algorithms, Stanford University (Youtube)
- CS 224 - Advanced Algorithms, Harvard University (Lecture Videos) (Youtube)
- CS 6150 - Advanced Algorithms (Fall 2016), University of Utah
- CS 6150 - Advanced Algorithms (Fall 2017), University of Utah
- ECS 222A - Graduate Level Algorithm Design and Analysis, UC Davis
- 6.851 - Advanced Data Structures, MIT (MIT OCW)
- 6.854 - Advanced Algorithms, MIT (Prof. Karger lectures)
- CS264 Beyond Worst-Case Analysis, Fall 2014 - Tim Roughgarden Lecture (Youtube)
- CS364A Algorithmic Game Theory, Fall 2013 - Tim Roughgarden Lectures
- CS364B Advanced Mechanism Design, Winter 2014 - Tim Roughgarden Lectures
- CS3510 Algorithms, Spring 2024 - Georgia Tech Videos - Sp 2024 Videos - Su 2025
- CS4510 Automata and Complexity - Georgia Tech Videos - Su 2021 Videos - Su 2023 Videos - Su 2024 Videos - Su 2025
- Algorithms - Aduni
- 6.889 - Algorithms for Planar Graphs and Beyond (Fall 2011) MIT
- 6.890 Algorithmic Lower Bounds: Fun with Hardness Proofs - MIT OCW
- Computer Algorithms - 2 - IIT Kanpur
- Parallel Algorithm - IIT Kanpur
- Graph Theory - IISC Bangalore
- Data Structures - mycodeschool
- Algorithmic Game Theory, Winter 2020/21 - Uni Bonn
- NETS 4120: Algorithmic Game Theory, Spring 2023 - UPenn
- Introduction to Game Theory and Mechanism Design - IIT Kanpur
- 15-850 Advanced Algorithms - CMU Spring 2023
- CS 270. Combinatorial Algorithms and Data Structures, Spring 2021 (Youtube)
- CMU 15 850 Advanced Algorithms spring 2023, by Anupam Gupta
- UC Berkeley CS 294-165 Sketching Algorithms fall 2020, by Jelani Nelson (Youtube)
- UIUC CS 498 ABD / CS 598 CSC Algorithms for Big Data fall 2020, by Chandra Chekuri
- Algorithms for Data Science spring 2021, by Anil Maheshwari
- CMU 15 859 Algorithms for Big Data fall 2020, by David Woodruff
- CO 642 Graph Theory - University of Waterloo
- COMS W4241 Numerical Algorithms spring 2006, by Henryk Wozniakowski - Columbia
- Bonn Algorithms and Uncertainty summer 2021, by Thomas Kesselheim
- Harvard Information Theory 2022, by Gregory Falkovich
- Math 510 - Linear Programming and Network Flows - Colorado State University
- LINFO 2266 Advanced Algorithms for Optimization 2021, by Pierre Schaus - UCLouvain
- MIT 6.5210 / 6.854 / 18.415 Advanced Algorithms Fall 2013, 2020, 2021, 2022, by David Karger (Spring 2016, by Ankur Moitra)
- CMU 10 801 Advanced Optimization and Randomized Algorithms spring 2014, by Suvrit Sra and Alex Smola
- Purdue CS 381 Fundamental Algorithms, by Kent Quanrud (Spring 2022)
- Purdue CS 390 ATA Fundamental Algorithms Advanced, by Kent Quanrud (Spring 2025)
- Purdue CS 580 Graduate Algorithms, by Kent Quanrud (Spring 2023) (Spring 2024)
- Purdue CS 588 Randomized Algorithms, by Kent Quanrud (Fall 2022) (Spring 2024)
- UC Santa Cruz CSE 101 Intro to Data Structures and Algorithms fall 2022, by Seshadhri Comandur (Fall 2020)
- UC Santa Cruz CSE 201 Analysis of Algorithms winter 2022, by Seshadhri Comandur
- UC Santa Cruz CSE 202 Combinatorial Algorithms spring 2021, by Seshadhri Comandur
- UC Santa Cruz CSE 104, 204 Computational Complexity spring 2022, by Seshadhri Comandur (Fall 2020)
- UC Santa Cruz CSE 290A Randomized Algorithms spring 2020, by Seshadhri Comandur
- University of Maryland CMSC351 Introduction to Algorithms, by Mohammad Hajiaghayi
- University of Maryland CMSC858F Network Algorithms and Approximations, by Mohammad Hajiaghayi (YouTube playlists)
- University of Maryland CMSC858M Algorithmic Lower Bounds: Fun with Hardness Proofs, by Mohammad Hajiaghayi (YouTube playlists)
- University of Maryland UMD DATA602 / MSML602 Principles of Data Science spring 2024, by Mohammad Hajiaghayi
- Algorithms for Big-Data (Fall 2020) - Saket Saurabh
- CS498ABD - Algorithms for Big Data - UIUC, Fall 2020
- Advanced Data Structures
- CS60025 Algorithmic Game Theory - IIT KGP - Winter 2020
- CS60083 Parameterized Algorithms - IIT KGP
- Parameterized Complexity
- Structural Graph Theory - IIT Madras
- Information Theory - IISC Bangalore
- 6.5220/6.856J/18.416J Randomized Algorithms (Spring 2025) (Youtube)
- 18.225 - Graph Theory and Additive Combinatorics - MIT - Fall 2023
- Extremal graph theory at KAIST
- Advanced graph theory, KAIST 2020
- COMP1521 - Computer Systems Fundamentals (2025) - UNSW (Full Course: Website + Lectures, Labs, Assignments, Exercises, Exams, Video Playlist) | Watch Lectures
- 15-213 Introduction to Computer Systems, Fall 2015 - CMU
- Computer Systems: A programmer's Perspective
- CS361 - COMPUTER SYSTEMS - UIC
- CS 3650 - Computer Systems - Fall 2020 - Nat Tuck - NEU (Lectures - YouTube)
- CS 4400 – Computer Systems Fall 2016 - UoUtah
- Systems - Aduni
- CS110: Principles of Computer Systems - Stanford
-
- Gatech CS6200 Intro to OS
- APS 105 - Computer Fundamentals - Winter 2025 - Jon Eyolfson - University of Toronto (Winter 2024)
- ECS 150 - Operating Systems and Systems Programming - Fall 2020 - Joël Porquet-Lupine - UC Davis
- ECE 344 - Operating Systems - Fall 2024 - Jon Eyolfson - University of Toronto (Fall 2024 Section 2) (Fall 2023) (Fall 2022)
- ECE 353 - Systems Software - Winter 2025 - Jon Eyolfson - University of Toronto (Winter 2024) (Winter 2023)
- ECE 454 - Computer Systems Programming - Fall 2024 - Jon Eyolfson - University of Toronto
- CS124 Operating Systems - California Institute of Technology, Fall 2018 - Youtube
- CS 162 Operating Systems and Systems Programming, Spring 2015 - University of California, Berkeley (Fall 2020 - YouTube)
- CS 4414 - Operating Systems, University of Virginia (rust-class)
- CS 4414 Operating Systems, Fall 2018 - University of Virginia
- CSE 421/521 - Introduction to Operating Systems, SUNY University at Buffalo, NY - Spring 2016 (Lectures - YouTube) (Recitations 2016) (Assignment walkthroughs)
- CS 377 - Operating Systems, Fall 16 - Umass OS
- CS 577 - Operating Systems, Spring 20 - Umass OS
- CS 537 - Introduction to Operating Systems - Andrea & Remzi Arpaci-Dusseau, University of Wisconsin-Madison (OSTEP free textbook) (Lecture Videos)
- 6.828 - Operating System Engineering [Fall 2014]
- 6.S081 - Operating System Engineering [Fall 2020]
- CSE 30341 - Operating Systems, Spr 2008
- CSEP 551 Operating Systems Autumn 2014 - University of Washington
- Introduction to Operating Systems - IIT Madras
- CS194 Advanced Operating Systems Structures and Implementation, Spring 2013 InfoCoBuild, UC Berkeley
- CSE 60641 - Graduate Operating Systems, Fall 08
- Advanced Programming in the UNIX Environment
- Operating System - IIT Madras
- CS 153 Frontier Systems - Stanford University - Spring 2026
- EE415: Introduction to Operating Systems (KAIST)
-
- CS 677 - Distributed Operating Systems, Spring 24 - Umass OS
- CS 436 - Distributed Computer Systems - U Waterloo
- 6.824 - Distributed Systems, Spring 2015 - MIT
- 6.824 Distributed Systems - Spring 2020 - MIT (Youtube)
- Distributed Systems Lecture Series
- Distributed Algorithms, https://canvas.instructure.com/courses/902299
- CSEP 552 - PMP Distributed Systems, Spring 2013 - University of Washington (Videos)
- CSE 490H - Scalable Systems: Design, Implementation and Use of Large Scale Clusters, Autumn 2008 - University of Washington (Videos)
- MOOC - Cloud Computing Concepts - UIUC
- Distributed Systems (Prof. Pallab Dasgupta)
- EdX KTHx ID2203 Reliable Distributed Algorithms
- Distributed Data Management - Technische Universität Braunschweig, Germany
- Information Retrieval and Web Search Engines - Technische Universität Braunschweig, Germany
- Middleware and Distributed Systems (WS 2009/10) - Dr. Martin von Löwis - HPI
- CSE 138 - Distributed Systems - UC Santa Cruz, Spring 2020 (2021)
- CMU 15 440 / 640 Distributed Systems Spring 2022, by Mahadev Satyanarayanan, Padmanabhan Pillai
- UNC Comp533 - Distributed Systems Spring 2020
- Brown CSCI 1380 Distributed Computer Systems spring 2016, by Tom Doeppner & Rodrigo Fonseca
- Distributed Systems lecture series - Martin Kleppmann
- Distributed Algorithms - Jukka Suomela
- Programming Parallel Computers - Jukka Suomela
- Gatech CS7210 Distributed Computing
- 6.172 Performance Engineering of Software Systems - MIT OCW
- Performance Evaluation of Computer Systems - IIT Madras
- Storage Systems - IISC Bangalore
- MAP6264 - Queueing Theory - FAU(Video Lectures)
- EE 380 Colloquium on Computer Systems - Stanford University (Lecture videos)
- CMPSC 431W Database Management Systems, Fall 2015 - Penn State University Lectures - YouTube
- CS121 - Introduction to Relational Database Systems, Fall 2016 - Caltech
- CS 5530 - Database Systems, Spring 2016 - University of Utah
- Distributed Data Management (WT 2018/19) - HPI University of Potsdam
- MOOC - Database Stanford Dbclass
- CSEP 544, Database Management Systems, Au 2015 - University of Washington
- Database Design - IIT Madras
- Fundamentals of Database Systems - IIT Kanpur
- Principles of Database Management, Bart Baesens
- FIT9003 Database Systems Design - Monash University
- 15-445 - Introduction to Database Systems, CMU (YouTube-2017), (YouTube-2018),(YouTube-2019), (YouTube-2021), (YouTube-2022),(YouTube-2023),(YouTube-2024),(YouTube-2025)
- 15-721 - Advanced Database Systems, CMU (YouTube-2024, YouTube-2023, YouTube-2022)
- CS122 - Relational Database System Implementation, Winter 2014-2015 - Caltech
- CS 186 - Database Systems, UC Berkeley, Spring 2015
- CS 6530 - Graduate-level Database Systems, Fall 2016, University of Utah (Lectures - YouTube)
- 6.830/6.814 - Database Systems Fall 2014
- Informatics 1 - Data & Analysis 2014/15- University of Edinburgh
- Database Management Systems, Aduni
- D4M - Signal Processing on Databases
- In-Memory Data Management (2013)Prof. Hasso Plattner - HPI
- Distributed Data Management (WT 2019/20) - Dr. Thorsten Papenbrock - HPI
- CS122d - NoSQL Data Management (Spring 21) - Prof. Mike Carey - UC Irvine
-
- ECE 462 Object-Oriented Programming using C++ and Java - Purdue
- Object-oriented Program Design and Software Engineering - Aduni
- OOSE - Object-Oriented Software Engineering, Dr. Tim Lethbridge
- Object Oriented Systems Analysis and Design (Systems Analysis and Design in a Changing World)
- CS 251 - Intermediate Software Design (C++ version) - Vanderbilt University
- OOSE - Software Dev Using UML and Java
- Object-Oriented Analysis and Design - IIT Kharagpur
- CS3 - Design in Computing - Richard Buckland UNSW
- Informatics 1 - Object-Oriented Programming 2014/15- University of Edinburgh
- Software Engineering with Objects and Components 2015/16- University of Edinburgh
-
- COMP1531 - Software Engineering Fundamentals - UNSW (2025) | Video Playlist
- Computer Science 169- Software Engineering - Spring 2015 - UCBerkeley
- Computer Science 169- Software Engineering - Fall 2019 - UCBerkeley
- CS 5150 - Software Engineering, Fall 2014 - Cornell University
- Introduction to Service Design and Engineering - University of Trento, Italy
- CS 164 Software Engineering - Harvard
- System Analysis and Design - IISC Bangalore
- Software Engineering - IIT Bombay
- Dependable Systems (SS 2014)- HPI University of Potsdam
- Automated Software Testing - ETH Zürich | Spring 2024
- Software Testing - IIT Kharagpur
- Software Testing - Udacity, course-cs258 | 2015
- Software Debugging - Udacity, course-cs259 | 2015
- Software Engineering - Bauhaus-Uni Weimar
- CMU 17-445 Software Engineering for AI-Enabled Systems summer 2020, by Christian Kaestner
-
- CS176 - Multiprocessor Synchronization - Brown University (Videos from 2012)
- CS 282 (2014): Concurrent Java Network Programming in Android
- CSE P 506 – Concurrency, Spring 2011 - University of Washington (Videos)
- CSEP 524 - Parallel Computation - University of Washington (Videos)
- Parallel Programming Concepts (WT 2013/14) - HPI University of Potsdam
- Parallel Programming Concepts (WT 2012/13) - HPI University of Potsdam
- UIUC ECE 408 / CS 408 Applied Parallel Programming fall 2022, by Wen-mei Hwu, Sanjay Patel (Spring 2018)
- UIUC ECE 508 / CS 508 Manycore Parallel Algorithms spring 2019, by Wen-mei Hwu
- UIUC CS 420 / ECE 492 / CSE 402 Introduction to Parallel Programming for Scientists and Engineers fall 2015, by Sanjay Kale
- Stanford CME 213 Introduction to Parallel Computing using MPI, openMP, and CUDA winter 2020, by Eric Darve
- KAIST CS431: Concurrent Programming
-
- MOOC Programming Mobile Applications for Android Handheld Systems - University of Maryland
- CS 193p - Developing Applications for iOS, Stanford University
- CS S-76 Building Mobile Applications - Harvard
- CS 251 (2015): Intermediate Software Design
- Android App Development for Beginners Playlist - thenewboston
- Android Application Development Tutorials - thenewboston
- MOOC - Developing Android Apps - Udacity
- MOOC - Advanced Android App Development - Udacity
- CSSE490 Android Development Rose-Hulman Winter 2010-2011, Dave Fisher
- iOS Course, Dave Fisher
- Developing iPad Applications for Visualization and Insight - Carnegie Mellon University
- Mobile Computing - IIT Madras
- Mobile Information Systems - Bauhaus-Uni Weimar
- CS50 - Introduction to Artificial Intelligence with Python (and Machine Learning), Harvard OCW
- 10-202: Introduction to Modern AI - CMU
- CS 188 - Introduction to Artificial Intelligence, UC Berkeley - Spring 2025, by John Canny, Oliver Grillmeyer (Spring 2024) (Spring 2023)
- 6.034 Artificial Intelligence, MIT OCW
- CS221: Artificial Intelligence: Principles and Techniques - Autumn 2019 - Stanford University
- 15-780 - Graduate Artificial Intelligence, Spring 14, CMU
- CSE 592 Applications of Artificial Intelligence, Winter 2003 - University of Washington
- CS322 - Introduction to Artificial Intelligence, Winter 2012-13 - UBC (YouTube)
- CS 4731/7632: Game AI, Spring 2021, Georgia Tech
- CS 4804: Introduction to Artificial Intelligence, Fall 2016
- CS 5804: Introduction to Artificial Intelligence, Spring 2015
- Artificial Intelligence, Fall 2023 - FAU (Spring 2023) (Fall 2022) (Spring 2021) (Fall 2020) (Fall 2018) (Spring 2018)
- Artificial Intelligence - IIT Kharagpur
- Artificial Intelligence - IIT Madras
- Artificial Intelligence(Prof.P.Dasgupta) - IIT Kharagpur
- MOOC - Intro to Artificial Intelligence - Udacity
- MOOC - Artificial Intelligence for Robotics - Udacity
- Graduate Course in Artificial Intelligence, Autumn 2012 - University of Washington
- Agent-Based Systems 2015/16- University of Edinburgh
- Informatics 2D - Reasoning and Agents 2014/15- University of Edinburgh
- Artificial Intelligence - Hochschule Ravensburg-Weingarten
- Deductive Databases and Knowledge-Based Systems - Technische Universität Braunschweig, Germany
- Artificial Intelligence: Knowledge Representation and Reasoning - IIT Madras
- Semantic Web Technologies by Dr. Harald Sack - HPI
- Knowledge Engineering with Semantic Web Technologies by Dr. Harald Sack - HPI
- T81-558: Applications of Deep Neural Networks by Jeff Heaton, 2022, Washington University in St. Louis
- MSU programming for AI
-
- Introduction to Machine Learning for Coders
- MOOC - Statistical Learning, Stanford University
- Statistical Learning with Python - Stanford Online
- Foundations of Machine Learning Boot Camp, Berkeley Simons Institute
- CS 155 - Machine Learning & Data Mining, 2023 - Caltech (Notes-2020) (YouTube-2020) (Notes-2019) (YouTube-2019) (Notes-2018) (YouTube-2018) (Notes-2017) (YouTube-2017) (Notes-2016) (YouTube-2016)
- CS 156 - Learning from Data, Caltech
- 10-601 - Introduction to Machine Learning (MS) - Tom Mitchell - 2015, CMU (YouTube)
- 10-601 Machine Learning | CMU | Fall 2017
- 10-701 - Introduction to Machine Learning (PhD) - Tom Mitchell, Spring 2011, CMU (Fall 2014) (Spring 2015 by Alex Smola) (Fall 2020 by Ziv Bar-Joseph, Eric Xing)
- 10 - 301/601 - Introduction to Machine Learning - Fall 2023 - CMU
- 6.036 - Machine Learning, Broderick - MIT Fall 2020
- Mediterranean Machine Learning summer school 2024 (YouTube-2023) (YouTube-2022) (YouTube-2021)
- LxMLS Lisbon Machine Learning School 2024 (YouTube-2023) (YouTube-2022) (YouTube-2021) (YouTube-2020)
- Applied Machine Learning (Cornell Tech CS 5787, Fall 2020)
- Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati) (Spring 2022)
- CMS 165 Foundations of Machine Learning - 2019 - Caltech (Youtube)
- CMS 165 Foundations of Machine Learning and Statistical Inference - 2020 - Caltech
- Microsoft Research - Machine Learning Course
- CS 446 - Machine Learning, Fall 2016, UIUC
- CS 582 - Machine Learning for Bioinformatics, Fall 2024, UIUC
- CMPUT 267 Machine Learning - Fall 2024 - University of Alberta (Youtube)
- ECE 364 - Programming Methods for Machine Learning, Spring 2025, UIUC
- EML 4930/5930 - Machine Learning: Introduction and Application, Fall 2025, FSU
- undergraduate machine learning at UBC 2012, Nando de Freitas
- CS 229 - Machine Learning - Stanford University (Autumn 2018)
- CSE 151A Introduction to Machine Learning, Prof Jingbo Shang - UCSD
- CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk - UCBerkeley
- CS 189/289A: Intro to Machine Learning - UC Berkeley - Spring 2026
- CPSC 340: Machine Learning and Data Mining (2018) - UBC
- CS391L Machine Learning, Spring 2025 - UT Austin
- CS4780/5780 Machine Learning, Fall 2013 - Cornell University
- CS4780/5780 Machine Learning, Fall 2018 - Cornell University (Youtube)
- CSE474/574 Introduction to Machine Learning - SUNY University at Buffalo
- CS 5350/6350 - Machine Learning, Spring 2024, University of Utah (Youtube)
- ECE 4252/8803 Fundamentals of Machine Learning (FunML), Spring 2024 - Georgia Tech
- ECE 5984 Introduction to Machine Learning, Spring 2015 - Virginia Tech
- CSx824/ECEx242 Machine Learning, Bert Huang, Fall 2015 - Virginia Tech
- STA 4273H - Large Scale Machine Learning, Winter 2015 - University of Toronto
- CSC 2515 Introduction to Machine Learning, Amir-massoud Farahmand, Fall 2021, University of Toronto
- ECE 421 Introduction to Machine Learning, Amir Ashouri, Winter 2019, University of Toronto
- EECS 4404E/5327 Introduction to Machine Learning, Amir Ashouri, Fall 2019, York University
- CS 480/680 Introduction to Machine Learning, Gautam Kamath, University of Waterloo (Spring 2021)
- CS 480/680 Introduction to Machine Learning, Kathryn Simone, University of Waterloo (Fall 2024)
- CS 485/685 Machine Learning, Shai Ben-David, University of Waterloo
- STAT 441/841 Classification Winter 2017 , Waterloo
- 10-605 - Machine Learning with Large Datasets, Fall 2016 - CMU
- Information Theory, Pattern Recognition, and Neural Networks - University of Cambridge
- Pattern Analysis (2018) - FAU (Class 2017) (Class 2016) (Class 2015) (Class 2009)
- Pattern Recognition (2020-2021) - FAU (Class 2012-2013)
- Beyond the Patterns (2020-2021) - FAU
- Python and machine learning - Stanford Crowd Course Initiative
- MOOC - Machine Learning Part 1a - Udacity/Georgia Tech (Part 1b Part 2 Part 3)
- Pattern Recognition Class (2012)- Universität Heidelberg
- Introduction to Machine Learning and Pattern Recognition - CBCSL OSU
- Introduction to Machine Learning - IIT Kharagpur
- Introduction to Machine Learning - IIT Madras
- Pattern Recognition - IISC Bangalore
- Pattern Recognition and Application - IIT Kharagpur
- Pattern Recognition - IIT Madras
- Machine Learning Summer School 2013 - Max Planck Institute for Intelligent Systems Tübingen
- Machine Learning - Professor Kogan (Spring 2016) - Rutgers
- CS273a: Introduction to Machine Learning (YouTube)
- Machine Learning Crash Course 2015
- COM4509/COM6509 Machine Learning and Adaptive Intelligence 2015-16
- Introduction to Machine Learning - Spring 2018 - ETH Zurich
- Machine Learning - Pedro Domingos- University of Washington
- CSE 446/546 - Machine Learning, Spring 2020 - University of Washington (Videos)
- Machine Learning (COMP09012)
- Probabilistic Machine Learning 2020 - University of Tübingen
- Statistical Machine Learning 2020 - Ulrike von Luxburg - University of Tübingen
- COMS W4995 - Applied Machine Learning - Spring 2020 - Columbia University
- Introductory Applied Machine Learning - Edinburgh University
- Machine Learning for Engineers 2022 (YouTube)
- 10-418 / 10-618 (Fall 2019) Machine Learning for Structured Data
- ORIE 4741/5741: Learning with Big Messy Data - Cornell
- Machine Learning in IoT
- Stanford CS229M: Machine Learning Theory - Fall 2021
- Intro to Machine Learning and Statistical Pattern Classification - Prof Sebastian Raschka
- CMU's Multimodal Machine Learning course (11-777), Fall 2020
- EE104: Introduction to Machine Learning - Stanford University
- CPSC 330: Applied Machine Learning (2020) - UBC
- Machine Learning 2013 - Nando de Freitas, UBC
- Machine Learning, 2014-2015, University of Oxford
- 10-702/36-702 - Statistical Machine Learning - Larry Wasserman, Spring 2016, CMU (Spring 2015)
- 10-715 Advanced Introduction to Machine Learning - CMU (YouTube)
- CS 281B - Scalable Machine Learning, Alex Smola, UC Berkeley
- 100 Days of Machine Learning - CampusX (Hindi)
- CampusX Data Science Mentorship Program 2022-23 (Hindi)
- Statistical Machine Learning - S2023 - Benyamin Ghojogh
- MIT 6.5940 EfficientML.ai Lecture, Fall 2023
- TinyML - Tiny Machine Learning at UPenn
- ECE 4760 (Digital Systems Design Using Microcontrollers) at Cornell for the Fall, 2022 (Spring 2021)
- EfficientML.ai Lecture, Fall 2023, MIT 6.5940
- SFU CMPT 727 Statistical Machine Learning, by Maxwell Libbrecht (Spring 2023) (Spring 2022)
- UC Berkeley CS 189 / 289A Introduction to Machine Learning fall 2023, by Jennifer Listgarten & Jitendra Malik
- UC Berkeley CS 189 Introduction to Machine Learning (CDSS offering) spring 2022, by Marvin Zhang
- UC San Diego/edX DSE 220X Machine Learning Fundamentals, by Sanjoy Dasgupta
- MIT 6.036 Introduction to Machine Learning spring 2019, by Leslie Kaelbling
- LMU Munich Introduction to Machine Learning
- CMU 15 388 / 15 688 Practical Data Science, by Zico Kolter (Fall 2019) (Spring 2018)
- UW Madison CS 320 Data Programming II spring 2021, by Tyler R. Caraza-Harter
- UC San Diego COGS9 Introduction to Data Science fall 2020, by Jason Fleischer
- UCLA Stats 15 Introduction to Data Science fall 2022, by Miles Chen
- UCLA Stats 21 Python and Other Technologies for Data Science spring 2024, by Miles Chen (Spring 2021)
- UCLA Stats C161/C261 Introduction to Pattern Recognition and Machine Learning winter 2024, by Arash Amini (Winter 2023)
- UCLA Stats 231C Theories of Machine Learning spring 2022, by Arash Amini
- Princeton COS 511 Theoretical Machine Learning spring 2026, by Elad Hazan
- MSU Machine Learning
- Data Science for Dynamical Systems, by Oliver Wallscheid & Sebastian Peitz (YouTube)
- Cambridge Statistical Learning in Practice 2021, by Alberto J. Coca
- Data 8: The Foundations of Data Science - UC Berkeley (Spring 23) (Fall 22) (Spring 22) (Summer 17)
- Data 144: Foundations of Data Science spring 2021 - Vassar College (Course materials)
- CSE519 - Data Science Fall 2016 - Skiena, SBU
- CS 109 Data Science, Harvard University (YouTube)
- 6.0002 Introduction to Computational Thinking and Data Science - MIT OCW
- Data 100: Principles and Techniques of Data Science - UC Berkeley (Fall 25) (Fall 24) (Spring 24) (Summer 19)
- Data 102 - Spring 21- UC Berkeley (YouTube)
- Distributed Data Analytics (WT 2017/18) - HPI University of Potsdam
- Data Profiling and Data Cleansing (WS 2014/15) - HPI University of Potsdam
- CS 229r - Algorithms for Big Data, Harvard University (Youtube)
- Algorithms for Big Data - IIT Madras
- Python Data Science with the TCLab (YouTube)
- Foundations of Data Analysis (Fall 2020)- University of Utah
- Introduction to Data Science (IDS) - Wil van der Aalst (RWTH Aachen University) (2021-2022 edition)
- Machine Learning Class (Winter 2019-2020) - UniHeidelberg
- Toronto ECE 1513 Introduction to Machine Learning winter 2026, by Ali Bereyhi
-
- CSEP 546, Data Mining - Pedro Domingos, Sp 2016 - University of Washington (YouTube)
- CS 5140/6140 - Data Mining, Spring 2020, University of Utah by Prof. Jeff Phillips (Youtube)
- CS 5140/6140 - Data Mining, Spring 2023, University of Utah by Prof. Ana Marasović (Youtube)
- CS 5955/6955 - Data Mining, University of Utah (YouTube)
- Statistics 202 - Statistical Aspects of Data Mining, Summer 2007 - Google (YouTube)
- MOOC - Text Mining and Analytics by ChengXiang Zhai
- Information Retrieval SS 2014, iTunes - HPI
- MOOC - Data Mining with Weka
- CS 290 DataMining Lectures
- CS246 - Mining Massive Data Sets, Winter 2016, Stanford University (YouTube)
- Information Retrieval - Spring 2018 - ETH Zurich
- Information Retrieval - WS 2022/23 - Universität Freiburg
- CAP6673 - Data Mining and Machine Learning - FAU(Video lectures)
- CS 412 - Introduction to Data Mining - UIUC
- CS 512 - Data Mining Principles - UIUC (YouTube)
-
- Full Stack Deep Learning - Course 2022
- Full Stack Deep Learning - Course 2021
- NYU Deep Learning Spring 2020
- NYU Deep Learning Spring 2021
- 6.S191: Introduction to Deep Learning - MIT
- 15.773: Hands-On Deep Learning Spring 2024 - MIT
- 6.7960: Deep Learning Fall 2024 - MIT
- Intro to Deep Learning and Generative Models Course - Prof Sebastian Raschka
- Deep Learning CMU
- CS231n Deep Learning for Computer Vision - Stanford University (Spring 2025) (Winter 2016 Andrej Karpathy)
- Deep Learning: CS 182 Spring 2021
- 10-414/714: Deep Learning Systems - CMU (Youtube)
- 11-785: Introduction to Deep Learning - CMU (Lectures - YouTube-2024, Recitations - YouTube-2024)
- Part 1: Practical Deep Learning for Coders, v3 - fast.ai
- Part 2: Deep Learning from the Foundations - fast.ai
- Deep learning at Oxford 2015 - Nando de Freitas
- Self-Driving Cars — Andreas Geiger, 2021/22 (YouTube)
- 6.S094: Deep Learning for Self-Driving Cars - MIT
- CS294-129 Designing, Visualizing and Understanding Deep Neural Networks (YouTube)
- CS230: Deep Learning - Autumn 2018 - Stanford University
- CS230: Deep Learning - Autumn 2025 - Stanford University
- STAT-157 Deep Learning 2019 - UC Berkeley
- Deep Learning, Stanford University
- MOOC - Neural Networks for Machine Learning, Geoffrey Hinton 2016 - Coursera
- Stat 946 Deep Learning - University of Waterloo
- EECS 298 Theory of Computational Neural Networks and Machine Learning (Fall 2020) - UC Irvine (YouTube)
- ECE 1508 Applied Deep Learning - University of Toronto (Fall 2025) (Winter 2025) (Fall 2024)
- ECE 1508 Reinforcement Learning - Fall 2025 - University of Toronto
- Neural networks class - Université de Sherbrooke (YouTube)
- DLCV - Deep Learning for Computer Vision - UPC Barcelona
- DLAI - Deep Learning for Artificial Intelligence @ UPC Barcelona
- Neural Networks and Applications - IIT Kharagpur
- UVA DEEP LEARNING COURSE
- Deep Learning - Winter 2020-21 - Tübingen Machine Learning
- Geometric Deep Learning - AMMI
- Math for Deep Learning — Andreas Geiger
- Applied Deep Learning 2025 - TU Wien (2024) (2023) (2022) (2021) (2020)
- Neural Networks: Zero to Hero - Andrej Karpathy
- CIS 522 - Deep Learning - U Penn
- UVA DEEP LEARNING COURSE
- Deep Learning (Fall 2020) - FAU (Spring 2020) (Fall 2019) (Spring 2019) (Fall 2018) (Spring 2018)
- Deep Learning (Fall 2020) - Georgia Tech
- Mathematics of Deep Learning (2021) - FAU
- CS7015 - Deep Learning - Prof. Mitesh M. Khapra - IIT Madras
- ETH Zürich | Deep Learning in Scientific Computing 2023
- Deep Learning François Fleuret
- Applied Deep Learning Maziar Raissi
- UC Berkeley CS 182 / 282a Deep Learning spring 2023, by Anant Sahai
- CMSC 828W Foundations of Deep Learning (Fall 2020) - UMD (YouTube)
- TUM IN2346 Introduction to Deep Learning Fall 2024, by Daniel Cremers (Summer 2023)
- UT Austin - Advances in Deep Learning
- HKU - Data 8014 Principles of Deep Representation Learning Fall 2025, by Yi Ma
- Local Explanations for Deep Learning Models (Fall 2023)
- CS 6966/5966 Interpretability of LLMs (Spring 2026) - University of Utah
- Deep Graph Learning (DGL, 2025) - Prof. Islem Rekik - Imperial College London
- Intuitive Deep Learning Series
- Toronto ECE 1508 Applied Deep Learning winter 2026, by Ali BereyhiYoutube
- UC Berkeley CS 182 / 282a Deep Learning fall 2025, by Gireeja Ranade & Anant SahaiYoutube
- SNU M2177.43 Introduction to Deep Learning spring 2026, by Hyun Oh Song
-
- CS234: Reinforcement Learning - Spring 2024 - Stanford University (Winter 2019)
- CSE 542: Reinforcement Learning - Spring 2024 - University of Washington
- CSE 579: Reinforcement Learning - Autumn 2024 - University of Washington
- CSC 2547: Introduction to Reinforcement Learning - Spring 2021 - University of Toronto (YouTube)
- Introduction to reinforcement learning - UCL
- Reinforcement Learning - IIT Madras (TA - Manav Mishra, TA - Prabhleen Kukreja, TA - Sandarbh Yadav , TA - Avik Kar)
- Special topics in ML (Reinforcement Learning) IIT madras
- CS885 Reinforcement Learning - Spring 2018 - University of Waterloo
- CS 224R - Deep Reinforcement Learning- Stanford (YouTube)
- CS 285 - Deep Reinforcement Learning- UC Berkeley (Spring 2026) (Fall 2019)
- CS 294 112 - Reinforcement Learning
- NUS CS 6101 - Deep Reinforcement Learning
- ECE 8851: Reinforcement Learning
- CS294-112, Deep Reinforcement Learning Sp17 (YouTube)
- UCL Course 2015 on Reinforcement Learning by David Silver from DeepMind (YouTube)
- DeepMind x UCL Reinforcement Learning Lecture Series 2021 - Hado van Hasselt, Diana Borsa, Matteo Hessel
- Deep RL Bootcamp - Berkeley Aug 2017
- Reinforcement Learning - IIT Madras
- Reinforcement Learning Course at KTH (FDD3359 - 2022)
- Reinforcement Learning Course at ASU, Spring 2022
- CS 4789/5789: Introduction to Reinforcement Learning - Cornell
- S20/IE613 - Online (Machine) Learning/ Bandit Algorithms
- Reinforcement Learning - Fall 2021 chandar-lab
- CMU 10 703 Deep Reinforcement Learning & Control fall 2022, by Katerina Fragkiadaki
- ECE524 Foundations of Reinforcement Learning at Princeton University, Spring 2024
- REINFORCEMENT LEARNING AND OPTIMAL CONTROL - Dimitri P. Bertsekas, ASU
- CMU 16 745 Optimal Control and Reinforcement Learning spring by Zac Manchester
- CMU 16 899 Adaptive Control and Reinforcement Learning fall 2020, by Changliu Liu
- Jadavpur University, 2025: Introduction to Reinforcement Learning
- EE675 (2024) Introduction to Reinforcement Learning Course | IIT Kanpur
- Reinforcement Learning Course by Frédéric Godin - Concordia University
- CS 285: Deep RL, 2023
- Mathematical Foundations of Reinforcement Learning - WINDY Lab
- Reinforcement Learning (HMC CS 181V)—Spring, 2020 - Neil Rhodes
- Reinforcement Learning Course: Lectures (Summer 2023) by Paderborn University
- CS292F (Spring 2021) Statistical Foundation of Reinforcement Learning - UCSD
- Algorithmic Foundations of Interactive Learning - CMU
-
- Advanced Machine Learning, 2021-2022, Sem I - by Prof. Madhavan Mukund, CMI
- 18.409 Algorithmic Aspects of Machine Learning Spring 2015 - MIT
- CS 330 - Deep Multi-Task and Meta Learning - Fall 2019 - Stanford University (Youtube)
- Stanford CS330: Deep Multi-Task and Meta Learning I Autumn 2022
- ES 661 (2023): Probabilistic Machine Learning - IIT Gandhinagar
- Information Retrieval in High Dimensional Data
- Trustworthy Machine Learning - Winter Semester 2023-2024, University of Tübingen
- Trustworthy Machine Learning - Winter Semester 2024-2025, University of Tübingen
- ETH Zürich Advanced Machine Learning fall 2019, by Joachim M. Buhmann
- CS 159 Advanced Topics in Machine Learning, Spring 2021 - Caltech
- CS 229br Advanced Topics in the theory of machine learning, Spring 2021 - Harvard
-
- CS 224N -Natural Language Processing with Deep Learning - Stanford University (Lectures - Winter 2019) (Lectures - Winter 2021) (Lectures - Spring 2024)
- CS 224N - Natural Language Processing, Stanford University (Lecture videos)
- Stanford XCS224U: Natural Language Understanding I Spring 2023
- CS388: Natural Language Processing - UT Austin
- CS 124 - From Languages to Information - Stanford University
- CS 6340/5340 - Natural Language Processing - University of Utah - Spring 2024 (Youtube)
- CSE 447/517 - Natural Language Processing - University of Washington - Winter 2024
- Neural Networks: Zero to Hero - Andrej Karpathy
- fast.ai Code-First Intro to Natural Language Processing (Github)
- MOOC - Natural Language Processing - Coursera, University of Michigan
- Natural Language Processing at UT Austin (Greg Durrett)
- CS224U: Natural Language Understanding - Spring 2019 - Stanford University
- Deep Learning for Natural Language Processing, 2017 - Oxford University
- Natural Language Processing - IIT Bombay
- CMU Advanced NLP Fall 2024 (Lectures - Fall 2024) (Lectures - Fall 2021)
- CMU Neural Nets for NLP 2021
- Natural Language Processing - Michael Collins - Columbia University
- CMU CS11-711 - Advanced Natural Language Processing (Lectures - Spring 2025)
- CMU CS11-737 - Multilingual Natural Language Processing
- UMass CS685: Advanced Natural Language Processing (Spring 2022)
- Natural Language Processing (CMSC 470)
- Stanford CS25 - Transformers United 2023
- Natural Language Processing (IN2361) - TUM
- Natural Language Processing (Spring 2024) - University of Utah
- Multilingual NLP 2020 - CMU
- Speech Technology - IIT Madras
- Deep Learning for NLP (KAIST AI605 Fall 2021)
-
- Stanford CS236: Deep Generative Models I 2023 I Stefano Ermon
- CS 6785 - Deep Generative Models - Cornell Tech, Spring 2023)
- ECE 498 / 598 - Deep Generative Models - UIUC, Fall 2025)
- Mathematical Foundations of Generative AI - IIT Madras
- Deep Generative Models - IISC
- A Course on Generative AI - Diffusion Models - Israel Institute of Technology
- MIT 6.S184 Flow Matching and Diffusion Models, 2025
- Course on Diffusion Models for Generative AI - UT Austin
- CS 492(C) Diffusion and Flow Models - Fall 2025 - KAIST (YouTube)
- Stanford CS336 Language Modeling from Scratch I 2025 - Stanford (2026)
- Stanford CME295 Transformers & LLMs - Autumn 2025 - Stanford
- Stanford CME296: Diffusion & Large Vision Models
- Introduction to large language models - IIT Madras
- Build a Large Language Model (From Scratch) by Sebastian Raschka
- Reinforcement Learning of Large Language Models - UCLA
- WING NUS CS6101 Large Language Models (T2310)
- CS 839: Foundation Models Fall 2025 - UW Madison (YouTube playlists)
- CS 886: Recent Advances on Foundation Models Winter 2024 - University of Waterloo
- UC Berkeley CS 194/294-196 Agentic AI Fall 2025 (YouTube, 2025) (Notes, 2024) (YouTube, 2024)
- UC Berkeley CS 194/294-267 Understanding Large Language Models Foundations and Safety spring 2024, by Dawn Song & Dan Hendrycks
- UC Berkeley CS 194/294-280 Advanced Large Language Model Agents Spring 2025 (YouTube)
- Introduction to Large Language Models (LLMs), IIT Delhi
- CMU 10423 S24: Generative AI Videos
- CMU 10799 S26: Diffusion & Flow Matching
- 6.S183: A Practical Introduction to Diffusion Models - MIT (YouTube, 2025 lectures)
- The Principles of Diffusion Models - Vizuara
- Building LLMs from scratch - Vizuara
- Build DeepSeek from Scratch - Vizuara
- Reasoning LLMs from Scratch - Vizuara
- KAIST CS492(C): Diffusion and Flow Models (Fall 2025) (2024)
- Mathematics of Generative Modelling (Spring 2024) - IIIT Hyderabad (2023)
- NYCU Deep Generative Models 2025
- COMPSCI 690L | Deep Generative Models | 2024 | Sajjad Amini
- ECE1508: Deep Generative Models - Summer 2025 - UofT
- 6.S980 Machine Learning for Inverse Graphics - Fall 2022 - MIT
- 6.S087 - Foundation Models and Generative AI - MIT
-
- CS 231n - Convolutional Neural Networks for Visual Recognition, Stanford University
- The Ancient Secrets of Computer Vision, University of Washington - Joseph Redmon (YouTube)
- CS 198-126: Modern Computer Vision Fall 2022 (UC Berkeley)
- Machine Learning for Robotics and Computer Vision, WS 2013/2014 - TU München (YouTube)
- COGSCI 1 - Intro to Cognitive Science Summer 2022 - UC Berkeley
- Informatics 1 - Cognitive Science 2015/16- University of Edinburgh
- Informatics 2A - Processing Formal and Natural Languages 2016-17 - University of Edinburgh
- NOC:Deep Learning For Visual Computing - IIT Kharagpur
- Extreme Classification
- EECS 498/598 - Deep Learning for Computer Vision - University of Michigan - Fall 2019 (Youtube)
- Computer Vision - FAU Spring 2021 (Spring 2018)
- CAP5415 Computer Vision - UCF Fall 2023
- CAP6412 Advanced Computer Vision - UCF Spring 2024 (Youtube)
- CU Boulder CSCI 5722 Computer Vision - CU Boulder Spring 2025 (Youtube)
- Advanced Deep Learning for Computer vision (ADL4CV) (IN2364) - TU Munich (Youtube)
- Advanced Deep Learning for Physics (ADL4P) - TU Munich
- Computer Vision III: Detection, Segmentation and Tracking (CV3DST) (IN2375) - TU Munich
- Lecture: Computer Vision, University of Tübingen - Andreas Geiger (YouTube)
-
- Optimisation for Machine Learning: Theory and Implementation (Hindi) - IIT
- Rochester DSCC 435 Optimization for Machine Learning fall 2023, by Jiaming Liang
- Princeton ELE539/COS512 Optimization for Machine Learning spring 2021, by Chi Jin
- UT Dallas CS 7301 Advanced Topics in Optimization for Machine Learning spring 2021, by Rishabh Iyer (YouTube)
- Convex Analysis, Summer 2021 - TU Braunschweig (YouTube)
- EE364a: Convex Optimization I - Stanford University
- EE364b: Convex Optimization II - Stanford University (YouTube)
- 10-725 Convex Optimization, Spring 2015 - CMU
- 10-725 Convex Optimization: Fall 2016 - CMU
- 10-725 Optimization Fall 2012 - CMU
- 10-801 Advanced Optimization and Randomized Methods - CMU (YouTube)
- AM 207 - Stochastic Methods for Data Analysis, Inference and Optimization, Harvard University
- MIT 6.S098 Applied Convex Optimization IAP 2022, by Alexandre Amice, Benoit Legat (YouTube)
- UVic Math 428 / 529 Discrete Optimization fall 2025, by Jonathan A. Noel (YouTube)
- University of Twente Discrete Optimization, by Marc Uetz (Fall 2020)
- UC Davis MAT 168 Optimization winter 2024, by Matthias Köppe
- Purdue University CHE 597 Computational Optimization spring 2025, by Can Li
- Purdue University ECE 695 Optimization for Deep Learning (OPT4DL), by Abolfazl Hashemi YouTube - Fa 25
- UCSD CS292F Convex Optimization Spring 2020, by Yu-Xiang Wang (Youtube)
- UIUC ECE 490 Introduction to Optimization fall 2020, by Venugopal V. Veeravalli (YouTube)
- University of Wisconsin-Madison CS/ECE/ISyE 524 Introduction to Optimization spring 2017-18, by Laurent Lessard
- University of Wisconsin-Madison ISyE/Math/CS/Stat 525 Linear Optimization fall 2021, by Alberto Del Pia
- University of Wisconsin-Madison ISyE/Math/CS 728 Integer Optimization (second part of the course) spring 2020
- Columbia IEOR E4007 Optimization Models and Methods 2005, by Garud Iyengar
- UC Berkeley EECS 127 / 227A Optimization Models in Engineering (Spring 2024 lectures)
- Modern Optimization and theory for Deep Learning
- Manifold Learning, Optimization and Information Geometry - Politecnico Milan - PhD level
- Robust Optimization and Essentials of Numerical Nonsmooth Optimization - EUROPT Summer School 2021
-
- CS294 Deep Unsupervised Learning Spring 2024
- Deep Unsupervised Learning -- Berkeley Spring 2020
- CS294-158 Deep Unsupervised Learning SP19
- UC San Diego COGS 118A Supervised Machine Learning fall 2020, by Jason Fleischer
- UC San Diego COGS 118B Unsupervised Machine Learning winter 2024, by Jason Fleischer
- UIUC STAT 437 Unsupervised Learning spring 2024, by Tori Ellison
- Johns Hopkins Unsupervised Learning spring 2017, by Rene Vidal
- Unsupervised Learning (STAT 841), Winter 2017
-
- Quantum Machine Learning | 2021 Qiskit Global Summer School
- CS 6955 - Clustering, Spring 2015, University of Utah
- Info 290 - Analyzing Big Data with Twitter, UC Berkeley school of information (YouTube)
- CS224W Machine Learning with Graphs | Spring 2021 | Stanford University
- 9.520 - Statistical Learning Theory and Applications, Fall 2015 - MIT
- EE531 - Statistical Learning Theory, Spring 2026 - KAIST
- Statistical Learning Theory, Spring 2019 - ETH Zürich
- Course on the Statistical Learning Theory, University of São Paulo, ICMC
- Reinforcement Learning - UCL
- Regularization Methods for Machine Learning 2016 (YouTube)
- Statistical Inference in Big Data - University of Toronto
- Reinforcement Learning - IIT Madras
- Statistical Rethinking Winter 2015 - Richard McElreath
- Foundations of Machine Learning - Blmmoberg Edu
- Introduction to reinforcement learning - UCL
- Statistical Foundation of Reinforcement Learning - UCSD, by Yu-Xiang Wang, Spring 2021 (Youtube)
- Web Information Retrieval (Proff. L. Becchetti - A. Vitaletti)
- Big Data Systems (WT 2019/20) - Prof. Dr. Tilmann Rabl - HPI
- Distributed Data Analytics (WT 2017/18) - Dr. Thorsten Papenbrock - HPI
- Introduction to Data-Centric AI - MIT
- Parallel Computing and Scientific Machine Learning
- Machine Learning System Design - System Design Fight Club
- CS 329S - Machine Learning Systems Design, Winter 2022 - Chip Huyen, Stanford University (Syllabus & lecture videos)
- UT Austin ECE 381V Bandits and Online Learning fall 2021, by Sanjay Shakkottai
- UCSD MATH 273B Information Geometry and its Applications winter 2022, by Melvin Leok
- Cornell ECE 5545 Machine Learning Hardware and Systems Spring 2022, by Mohamed Abdelfattah
- High Dimensional Analysis: Random Matrices and Machine Learning by Roland Speicher(Youtube)
- ACP SUMMER SCHOOL 2023 on Machine Learning for Constraint Programming
- EE512A - Advanced Inference in Graphical Models, Fall Quarter, 2014
- University of Wisconsin-Madison CS/ECE 561 - Probability and Information Theory in Machine Learning fall 2020, by Matthew Malley
- University of Maryland CMSC828U Algorithms in Machine Learning: Guarantees and Analyses fall 2020, by Furong Huang (YouTube playlist)
- Statistical Physics of Machine Learning
- 11-755 - Machine Learning for Signal Processing, CMU (YouTube-2024, YouTube-2023)
- Machine Learning for 3D Data, Fall 2023, KAIST
- Machine Learning for 3D Data, Spring 2026, KAIST Recordings
- Machine Learning for Physicists, Spring 2019, FAU (Spring 2017)
- CSCE 585 - Machine Learning Systems, University of South Carolina (YouTube-2020)
- CS-E4740 - Federated Learning, Spring 2023, Aalto University
- Uncertainty Modeling in AI | National University of Singapore
- Process Mining Course @ RWTH Aachen University (BPI 2021)
- CS 2881 AI Safety - Harvard - Fall 2025 (Youtube)
- Chat GPT-X and Generative Models for EM Systems Design - KAIST
- CS492(F) Computational Learning Theory (Fall 2021, KAIST)
- CS 144 Introduction to Computer Networking - Stanford University, Fall 2013 (Lecture videos)
- Computer Networking: A Top-Down Approach
- Computer Communication Networks, Rensselaer Polytechnic Institute - Fall 2001 (Videos) (Slides)
- Audio/Video Recordings and Podcasts of Professor Raj Jain's Lectures - Washington University in St. Louis (YouTube)
- Computer Networks, Tanenbaum, Wetherall Computer Networks 5e - Video Lectures
- CSEP 561 - PMP Network Systems, Fall 2013 - University of Washington (Videos)
- CSEP 561 – Network Systems, Autumn 2008 - University of Washington (Videos)
- ECE/CS 438 - Communication Networks, Fall 2020 - UIUC
- Computer Networks - IIT Kharagpur
- Introduction to Data Communications 2013, Steven Gordon - Thammasat University, Thailand
- Introduction to Complex Networks - RIT
- Computer Networks and Internet Protocol - IIT Kharagpur
- Structural Analysis and Visualization of Networks
- Columbia ELEN E4703 Wireless Communications spring 2006, by Angel Lozano
- Columbia COMS W4119 Computer Networks fall 2004, by Vishal Misra
- Columbia ELEN E4710 An Introduction to Network Engineering fall 2004, by Dan Rubenstein (Videos)
- Data Communication - IIT Kharagpur
- Error Correcting Codes - IISC Bangalore
- Information Theory and Coding - IIT Bombay
- Complex Network : Theory and Application - IIT Kharagpur
- Advanced 3G and 4G Wireless Mobile Communications - IIT Kanpur
- Broadband Networks: Concepts and Technology - IIT Bombay
- Coding Theory - IIT Madras
- Digital Communication - IIT Bombay
- Digital Voice & Picture Communication - IIT Kharagpur
- Wireless Ad Hoc and Sensor Networks - IIT Kharagpur
- Internetworking with TCP/IP by Prof. Dr. Christoph Meinel - HPI
- CS798: Mathematical Foundations of Computer Networking - University of Waterloo
- CS 168 Introduction to the Internet: Architecture and Protocols, Fall 2022 - UC Berkeley (YouTube - Fall 2022) (Spring 2025)
- Advanced Topics in Communication Networks, Fall 2022 - ETH Zürich
- CS/ECE 438 Communication Networks [F23] - UIUC
- Maths courses all topics covered - Khan Academy
- Calculus
- Discrete Math
- 6.042J - Mathematics for Computer Science, MIT OCW
- 18.200 - Principles of Discrete Applied Mathematics, MIT OCW
- CS 2050 Discrete Mathematics, Georgia Tech Videos - Su 2024
- UC Berkeley Computer Science 70, Discrete Mathematics and Probability Theory - Fall 2025 (YouTube)
- CSE 547 Discrete Mathematics, Prof Skiena, University of Stony Brook
- Discrete Structures (Summer 2011) - Rutgers, The State University of New Jersey
- Discrete Mathematics and Mathematical Reasoning 2015/16 - University of Edinburgh
- Discrete Mathematical Structures - IIT Madras
- Discrete Structures - Pepperdine University
- CMU 21 228 Discrete Mathematics spring 2021, by Po-Shen Loh
- COMP2804: Discrete Structures II
- Probability & Statistics
- Statistics - CrashCourse
- 6.041 Probabilistic Systems Analysis and Applied Probability - MIT OCW
- Stanford CS109 Introduction to Probability for Computer Scientists I 2022 I Chris Piech
- MIT RES.6-012 Introduction to Probability, Spring 2018 - MIT
- Statistics 110 - Probability - Harvard University
- Statistical Rethinking 2026 - Richard McElreath
- STAT 2.1x: Descriptive Statistics | UC Berkeley
- STAT 2.2x: Probability | UC Berkeley
- MOOC - Statistics: Making Sense of Data, Coursera
- MOOC - Statistics One - Coursera
- Probability and Random Processes - IIT Kharagpur
- MOOC - Statistical Inference - Coursera
- 131B - Introduction to Probability and Statistics, UCI
- STATS 250 - Introduction to Statistics and Data Analysis, UMichigan
- Sets, Counting and Probability - Harvard
- Opinionated Lessons in Statistics (Youtube)
- Statistics - Brandon Foltz
- Statistical Rethinking: A Bayesian Course Using R and Stan (Lectures) (Book)
- 02402 Introduction to Statistics E12 - Technical University of Denmark (F17)
- Engineering Probability (ECSE-2500) - RPI
- Purdue ECE302 Introduction to Probability for Data Science
- Undergraduate Probability with Professor Roman Vershynin
- High-Dimensional Probability
- Mathematical Statistics - 2024 (YouTube-2020)
- Bayesian Data Analysis
- Bayesian Machine Learning and Information Processing (YouTube-2021/22) (YouTube-2020/21)
- Markov Processes - Spring 2023
- Measure Theoretic Probability
- Causal Inference Course - Brady Neal
- Causal Inference -- Online Lectures (M.Sc/PhD Level)
- Machine Learning & Causal Inference: A Short Course
- Causal Inference Jonas Peters
- UIUC ECE 534 Random Processes fall 2020 - Ilan Shomorony
- ISyE 320 Simulation and Probabilistic Modeling spring 2022, by Qiaomin Xie - University of Wisconsin-Madison
- Cambridge Principles of Statistics 2020, by Alberto J. Coca
- UC Berkeley STAT 150 Stochastic Processes spring 2021, by Brett Kolesnik
- UIUC Math 564 Applied Stochastic Processes fall 2016, by Kay Kirkpatrick
- CS/ECE 561 - Probability and Info Theory in Machine Learning
- UCLA Stats 10 Introduction to Statistical Reasoning summer 2022, by Miles Chen
- UCLA Stats 101C Statistical Models and Data Mining summer 2022, by Miles Chen
- UCLA Stats 102A Introduction to Computational Statistics with R winter 2024, by Miles Chen
- UCLA Stats 102B Computation and Optimization for Statistics spring 2024, by Miles Chen
- UCLA Stats 102C Introduction to Monte Carlo Methods fall 2023, by Miles Chen
- UCLA Stats 200B Theoretical Statistics winter 2024, by Arash Amini (Winter 2023)
- UCLA Stats 200C High-dimensional Statistics spring 2022, by Arash Amini (Spring 2021)
- UCLA Stats 203 Large Sample Theory fall 2021, by Jingyi Jessica Li (Fall 2020)
- UCSD Math 280 Probability Theory and Stochastic Processes, by Todd Kemp (YouTube)
- METU EE 531 Probability and Stochastic Processes, by Elif Uysal
- 6.262 Discrete Stochastic Processes - MIT OCW
- 18.650 Statistics for Applications - MIT OCW
- STAT240 - Robust Statistics - UC Berkeley
- MAS575 Combinatorics at KAIST
- Probabilistic method, KAIST 2020
- Linear Algebra
- Mathematical Foundations of Machine Learning (Fall 2021) - University of Chicago - Rebecca Willett
- Mathematical Foundations of Machine Learning (Fall 2025) - Rebecca Willett - University of Chicago
- 18.06 - Linear Algebra, Prof. Gilbert Strang, MIT OCW
- 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning - MIT OCW
- University of Wisconsin-Madison ECE/CS/ME 532 Matrix Methods in Machine Learning fall 2017, by Laurent Lessard
- Linear Algebra (Princeton University)
- MOOC: Coding the Matrix: Linear Algebra through Computer Science Applications - Coursera
- CS 053 - Coding the Matrix - Brown University (Fall 14 videos)
- Linear Algebra Review - CMU
- A first course in Linear Algebra - N J Wildberger - UNSW
- INTRODUCTION TO MATRIX ALGEBRA
- Computational Linear Algebra - fast.ai (Github)
- ENGR108: Introduction to Applied Linear Algebra—Vectors, Matrices, and Least Squares - Stanford University
- MIT 18.S096 Matrix Calculus For Machine Learning And Beyond
- Cornell MATH 2940 Linear Algebra for Engineers spring 2009, by Andy Ruina
- 10-600 Math Background for ML - CMU
- MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
- Direct Methods for Sparse Linear Systems - Prof Tim Davis - UFL
- 36-705 - Intermediate Statistics - Larry Wasserman, CMU (YouTube)
- Combinatorics - IISC Bangalore
- Advanced Engineering Mathematics - Notre Dame
- Statistical Computing for Scientists and Engineers - Notre Dame
- Statistical Computing, Fall 2017 - Notre Dame
- Statistics 243 Introduction to Statistical Computing, Fall 2015 - UC Berkeley (Notes-2015) (YouTube-2014) (Notes-2014) (YouTube-2013)
- Mathematics for Machine Learning, Lectures by Ulrike von Luxburg - Tübingen Machine Learning
- Essential Mathematics for Machine Learning- July 2018 - IIT Roorkee - YouTube Lectures
- Numerics of Machine Learning (Winter 2022/23) - Tübingen Machine Learning
- Nonlinear Dynamics and Chaos - Steven Strogatz, Cornell University
- Nonlinear Dynamics & Chaos - Virginia Tech
- An introduction to Optimization on smooth manifolds (with book) - EPFL
- Math Modelling
- Large-Scale Convex Optimization: Algorithms & Analyses via Monotone Operators by Ernest Ryu and Wotao Yin
- An Overview of Variational Analysis 2021 by Tyrrell Rockafellar
- UW AMATH 584 Applied Linear Algebra & Numerical Analysis by Nathan Kutz
- UW AMATH 584 Applied Linear Algebra & Introductory Numerical Analysis fall 2005, by Loyce Adams
- Stanford CME 206 Introduction to Numerical Methods for Engineering spring 2005, by Charbel Farhat
- Stanford CME 200 Linear Algebra with Application to Engineering Computations autumn 2004, by Margot Gerritsen
- Stanford CME 302 Numerical Linear Algebra autumn 2007, by Gene Golub
- TUe Numerical Linear Algebra 2021, by Martijn Anthonissen
- Numerical Linear Algebra fall 2018, by Jaegul Choo
- MIT 6.S955 Applied Numerical Algorithms fall 2023, by Justin Solomon
- MIT 6.7350 Numerical Algorithms for Computing and Machine Learning fall 2025, by Justin Solomon
- UC Berkeley Math 54 Linear Algebra & Differential Equations spring 2022, by Alexander Paulin (Summer 2021, by Peter Koroteev) (Summer 2020, by Luvreet Sangha) (Spring 2018, by Alexander Paulin)
- UC Berkeley Math 55 Discrete Mathematics fall 2021, by Nikhil Srivastava
- UC Berkeley Math 56 Linear Algebra fall 2023, by Alexander Paulin
- Fundamental Mathematics for Robotics spring 2020, by Ken Tomiyama
- Short Course on Casual Inference, by Sanjay Shakkottai
- UCLA STAT 100C Linear Models spring 2023, by Arash Amini
- MSU Math for Computing
- Mathematics of Data Science - ETH Zurich
- Mathematical Data Science 1 - Spring 2021 - FAU (Spring 2020)
- Engineering Mathematics (UW ME564 and ME565) - Steve Brunton
- Beginning Scientific Computing - Steve Brunton
- Jadavpur University: Foundation_Math_forML_Autumn23
- FAU: Inverse Problems Autumn21
- EdX 1st Course on SparseLand by Michael Elad - Theory
- EdX 2nd Course on SparseLand by Michael Elad - Practice
- CS50's Web Programming with Python and JavaScript
- Web Design Decal - HTML/CSS/JavaScript Course, University of California, Berkeley
- CS 75 Building Dynamic Websites - Harvard University
- Internet Technology - IIT Kharagpur
- Introduction to Modern Application Development - IIT Madras
- CSE 199 - How the Internet Works, Fall 2016 - University of Buffalo
- Open Sourced Elective: Database and Rails - Intro to Ruby on Rails, University of Texas (Lectures - Youtube)
- CSE154 - Web Programming, Spring 2020 - University of Washington (Videos)
- CSEP545 - Transaction Processing for E-Commerce, Winter 2012 - University of Washington (Videos)
- CT 310 Web Development - Colorado State University
- Internet Technologies and Applications 2012, Steven Gordon - Thammasat University, Thailand
- CSCI 3110 Advanced Topics in Web Development, Fall 2011 - ETSU iTunes
- CSCI 5710 e-Commerce Implementation, Fall 2015 - ETSU iTunes
- MOOC - Web Development - Udacity
- Web Technologies Prof. Dr. Christoph Meinel - HPI
- Full Stack Open - University of Helsinki (Modern Web Development with React, Node.js, GraphQL, TypeScript)
- MIT 18.404J Theory of Computation - Fall 2020 - Lecture Slides
- MIT 18.404J Theory of Computation - Fall 2020 - Video Lectures
- MOOC - Compilers - Stanford University
- CS 6120: Advanced Compilers: The Self-Guided Online Course - Cornell University
- CS 164 Hack your language, UC Berkeley (Lectures - Youtube)
- Theory of computation - Shai Simonson
- CS 173 Programming Languages, Brown University (Book)
- CS Theory Toolkit at CMU 2020
- CS 421 - Programming Languages and Compilers, UIUC
- CSC 253 - CPython internals: A ten-hour codewalk through the Python interpreter source code, University of Rochester
- CSE341 - Programming Languages, Dan Grossman, Spring 2013 - University of Washington
- CSEP 501 - Compiler Construction, University of Washington (Lectures - Youtube)
- CSEP 505 Programming Languages, Winter 2015 - University of Washington
- DMFP - Discrete Mathematics and Functional Programming, Wheaton College
- CS 374 - Algorithms & Models of Computation (Fall 2014), UIUC (Lecture videos)
- 6.045 Automata, Computability, and Complexity, MIT (Lecture Videos)
- MOOC - Automata - Jeffrey Ullman - Coursera
- CS581 Theory of Computation - Portland State University (Lectures - Youtube)
- Theory of Computation - Fall 2011 UC Davis
- TDA555 Introduction to Functional Programming - Chalmers University of Technology (Lectures - YouTube)
- Ryan O'Donnell Theoretical Computer Science Talks
- Philip Wadler Haskell lecture recordings
- Functional Programming (2021) - University of Nottingham
- Functional Programming - University of Edinburgh - 2016-17
- MOOC - Functional Programming Principles in Scala by Martin Odersky
- CS294 - Program Synthesis for Everyone
- MOOC - Principles of Reactive Programming, Scala - Coursera
- Category Theory for Programmers, 2014 - Bartosz Milewski (YouTube)
- Oregon Programming Languages Summer School (Proof theory, type theory, category theory, verification)
- Inf1 - Computation and Logic 2015 - University of Edinburgh
- INFORMATICS 1 - FUNCTIONAL PROGRAMMING - University of Edinburgh (Videos)
- Compiler Design - IISC Bangalore
- Compiler Design - IIT Kanpur
- Principles of Programming Languages - IIT Delhi
- Principles of Compiler Design - IISC Bangalore
- Functional Programming in Haskell - IIT Madras
- Theory of Computation - IIT Kanpur
- Theory of Automata, Formal Languages and Computation - IIT Madras
- Theory of Computation - IIT Kanpur
- Logic for CS - IIT Delhi
- Principles of Compiler Design - Swarthmore College
- Undergrad Complexity Theory at CMU
- Graduate Complexity Theory at CMU
- Great Ideas in Theoretical Computer Science at CMU
- Analysis of Boolean Functions at CMU
- Theoretical Computer Science (Bridging Course)(Tutorial) - SS 2015
- Languages & Translators - UCLouvain LINFO2132
- Compiler Design by Sorav Bansal
- OCaml Programming: Correct + Efficient + Beautiful
- Columbia IEOR E4004 Introduction to Operations Research: Deterministic Models summer 2005, by Jay Sethuraman
- Columbia IEOR E4106 Introduction to Operations Research: Stochastic Models spring 2005, by Ward Whitt
- Columbia ELEN E6711 Stochastic Models in Information Systems fall 2005, by Yuliy Barsyhnikov
- Columbia ELEN E6717 Information Theory fall 2003, by Vittorio Castelli
- University of Washington EE514A/EE515A - Information Theory I/II fall 2013, by Jeff Bilmes
- CMU 15 150 Principles of Functional Programming summer 2023, by Brandon Wu
- CMU 21 738 Extremal Combinatorics spring 2020, by Po-Shen Loh
- JHU Domain-Specific Languages (DSL) Class (Summer 2018)
- Spring 2019 - Probabilistically Checkable and Interactive Proof Systems (Alessandro Chiesa)
- Algebraic Coding Theory - Stanford University
- CS60094 Computational Number Theory
- KAIST CS420: Compiler Design
- CS402 Introduction to Logic for Computer Science (Spring 2020, KAIST)
- CS520 Theories of Programming Languages (Fall 2020, KAIST)
- EE319K Embedded Systems - UT Austin
- EE445L Embedded Systems Design Lab, Fall 2015, UTexas
- CS149 Introduction to Embedded Systems - Spring 2011 - UCBerkeley
- CSE/ECE 474 Introduction to Embedded Systems - University of Washington (Lectures - YouTube-Spring 21)
- ECE 4760 Designing with Microcontrollers Fall 2016, Cornell University (Lectures - Youtube)
- ECE 5760 - Advanced Microcontroller Design and system-on-chip, Spring 2016 - Cornell University
- Internet of Things by Prof. Dr.-Ing. Dietmar P. F. Möller
- 18 642 - Embedded Software Engineering, Fall 2021 - CMU
- CSE 351 - The Hardware/Software Interface, Spring 16 - University of Washington (Coursera)
- ECE 5030 - Electronic Bioinstrumentation, Spring 2014 - Cornell University
- ECE/CS 5780/6780 - Embedded Systems Design, Spring 14 - University of Utah
- EECS 373 - Introduction to Embedded System Design - University of Michigan (Lectures - YouTube-Fall 24) (Lectures - YouTube-Fall 23)
- Embedded Systems Class - Version 1 - 2011 - UNCC
- Embedded Systems using the Renesas RX63N Processor - Version 3 - UNCC
- Software Engineering for Embedded Systems (WS 2011/12) - HPI University of Potsdam
- Embedded Software Testing - IIT Madras
- Embedded Systems - IIT Delhi
- Embedded Systems Design - IIT Kharagpur
- ARM Based Development - IIT Madras
- Software Engineering for Self Adaptive Systems - iTunes - HPI University of Potsdam
- EE260 Embedded Systems by Robert Paz
- IoT Summer School
- ECSE 421 - Embedded Systems - McGill
- NOC:Advanced IOT Applications - IISc Bangalore
- NOC:Design for internet of things - IISc Bangalore
- Performance evaluation of Computer systems - IIT Madras
- Real Time systems - IIT Karaghpur
- EE 380 Colloquium on Computer Systems - Stanford University
- CSE 6220 / CX 4220 Introduction to High Performance Computing - Georgia Tech Videos - Sp 2025 Website - Fa 2024 Videos - Fa 2024
- CSE 6230 High Performance Computing: Tools and Applications - Georgia Tech Videos - Sp 2024
- System storages - IISc Bangalore
- High Performance Computing - IISC Bangalore
- 2023 High Performance Computing Course Prof Dr - Ing Morris Riedel (2022)
- High Performance Computing | Udacity
- UCLA Stats 205 Hierarchical Linear Models spring 2024, by Jingyi Jessica Li
- Stanford AA 203 Optimal and Learning-Based Control, by Marco Pavone, Daniele Gammelli (Videos - Sp 2026)
- FSU EML 4930 / 5930 Applied Optimal Control spring 2025, by Christian Hubicki (Spring 2023)
- UF EML 6934 Optimal Control spring 2012, by Anil V. Rao
- Real-World Algorithms for IoT and Data Science - UIUC
- Computer Organization
- How Computers Work - Aduni
- CS 61C - Machine Structures, UC Berkeley Spring 2015
- 6.004 - Computation Structures Spring 2013, MIT
- CS/ECE 3810 Computer Organization, Fall 2015, , University of Utah (YouTube)
- Digital Computer Organization - IIT Kharagpur
- Computer Organization - IIT Madras
- CS-224 - Computer Organization, 2009-2010 Spring, Bilkent University (YouTube playlist)
- INFORMATICS 2C - INTRODUCTION TO COMPUTER SYSTEMS (AUTUMN 2016) - University of Edinburgh
- Computer Architecture
- 18-447 - Introduction to Computer Architecture, CMU (Lectures - YouTube - Fall 15)
- CSEP 548 - Computer Architecture Autumn 2012 - University of Washington
- CS/ECE 6810 Computer Architecture, Spring 2016, University of Utah (YouTube)
- MOOC - Computer Architecture, David Wentzlaff - Princeton University/Coursera
- Computer Architecture - ETH Zürich - Fall 2019
- Digital Circuits and Computer Architecture - ETH Zurich - Spring 2017
- Computer Architecture - IIT Delhi
- Computer Architecture - IIT Kanpur
- Computer Architecture - IIT Madras
- High Performance Computer Architecture - IIT Kharagpur
- BE5B35APO - Computer Architectures, Spring 2025, CTU - FEE (RISC-V simulator - QtRvSim), (YouTube - Spring 2025), (YouTube - Spring 2022)
- BE4M35PAP - Advanced Computer Architectures, Winter 2025, CTU - FEE (VHSky.cz - Winter 2025)
- CS773: Computer Architecture for Performance and Security - IIT Bombay
- COL718 - Architecture of High Performance Computers - IIT Delhi
- CS 695: Virtualization and Cloud Computing - IIT Bombay -Spring 2021
- Parallel Computer Architecture
- 15-418 - Parallel Computer Architecture and Programming, CMU (Lecture Videos)
- CS 267 Applications of Parallel Computers, Spring 18 - UC Berkeley (YouTube-Spring 18) (Notes-Spring 16) (YouTube-Spring 16)
- MOOC - Heterogeneous Parallel Programming - Coursera
- ECE 498AL - Programming Massively Parallel Processors
- ECE 1756 - Reconfigurable Computing and FPGA Architecture - University of Toronto - Fall 2020 (YouTube-Fall 2022)
- Parallel Computing - IIT Delhi
- Parallel Architectures 2012/13- University of Edinburgh
- Digital Systems Design
- CS1 - Higher Computing - Richard Buckland UNSW
- MOOC - From NAND to Tetris - Building a Modern Computer From First Principles (YouTube)
- System Validation, TU Delft
- High Performance Computing - IISC Bangalore
- Introduction to ARM - Open SecurityTraining
- Intro x86 (32 bit) - Open SecurityTraining
- Intermediate x86 (32 bit) - Open SecurityTraining
- EECS 151 / 251A Introduction to Digital Design and Integrated Circuits - UC Berkeley - Fall 2025
- Design of Digital Circuits - ETH Zürich - Spring 2019
- Onur Mutlu @ TU Wien 2019 - Memory Systems
- Memory Systems Course - Technion, Summer 2018
- UC Berkeley EECS16A Designing Information Devices and Systems I summer 2020, by Grace Kuo, Panos Zarkos, Urmita Sikder
- UC Berkeley EECS 16B Designing Information Devices and Systems II fall 2020, by Seth Sanders, Miki Lustig
- EE 503 - Statistical Signal Processing and Modeling - Fall 2020 - METU (YouTube)
- ELEN E4810 - DIGITAL SIGNAL PROCESSING - Fall 2013 - Columbia
- ELEN E4896 - MUSIC SIGNAL PROCESSING - Spring 2016 - Columbia
- Columbia ELEN E6820 Speech and Audio Processing spring 2006, by Dan Ellis
- CMU 11 751 / 18 781 Speech Recognition and Understanding fall 2023, by Shinji Watanabe (Fall 2022)
- CMU 11 492 Speech Processing fall 2021, by Alan W. Black
- Internet Security (WT 2018/19) - HPI University of Potsdam
- 6.1600 Foundations of Computer Security - MIT Fall 2023
- 6.858 Computer Systems Security - MIT OCW
- CS 253 Web Security - Stanford University
- CS 161: Computer Security, UC Berkeley (Videos - Fall 2023) (Videos - Fall 2025) (Spring 2025)
- 6.875 - Cryptography - Fall 2021 - MIT (Spring 2018)
- CSEP590A - Practical Aspects of Modern Cryptography, Winter 2011 - University of Washington (Videos)
- CS461/ECE422 - Computer Security - University of Illinois at Urbana-Champaign (Videos)
- Introduction to Cryptography, Christof Paar - Ruhr University Bochum, Germany
- ECS235B Foundations of Computer and Information Security - UC Davis
- CIS 4930/ CIS 5930 - Offensive Computer Security, Florida State University
- Introduction to Information Security I - IIT Madras
- Information Security - II - IIT Madras
- Introduction to Cryptology - IIT Roorkee
- Cryptography and Network Security - IIT Kharagpur
- 18-636 Browser Security, Stanford
- Internet Security - Weaknesses and Targets (WT 2015/16) (WT 2012/13 (YouTube))
- IT Security, Steven Gordon - Thammasat University, Thailand
- Security and Cryptography, Steven Gordon - Thammasat University, Thailand
- MOOC - Cryptography - Coursera
- MOOC - Intro to Information Security - Udacity
- ICS 444 - Computer & Network Security
- Privacy and Security in Online Social Networks - IIT Madras
- Malware Dynamic Analysis - Open SecurityTraining (YouTube)
- CSN09112 - Network Security and Cryptography - Bill Buchanan - Edinburgh Napier
- CSN10107 - Security Testing and Network Forensics - Bill Buchanan - Edinburgh Napier
- CSN11123 - Advanced Cloud and Network Forensics - Bill Buchanan - Edinburgh Napier
- CSN11117 - e-Security - Bill Buchanan - Edinburgh Napier
- CSN08704 - Telecommunications - Bill Buchanan - Edinburgh Napier
- CSN11128 - Incident Response and Malware Analysis - Bill Buchanan - Edinburgh Napier
- Internet Security for Beginners by Dr. Christoph Meinel - HPI
- Offensive Security and Reverse Engineering, Chaplain University by Ali Hadi
- Computer Systems Security, Fall 2020, Vinod Ganapathy, IISc Bangalore
- UC Berkeley CS 161 Computer Security, Summer 2021, by Nicholas Ngai and Peyrin Kao
- UCSD CS291A Differential Privacy Fall 2021, by Yu-Xiang Wang (Youtube)
- Zero Knowledge Proofs MOOC, UC Berkeley RDI Center on Decentralization & AI
- Multimedia Security, Fall 2017, FAU
- ECS 175 - Computer Graphics, Fall 2009 - UC Davis
- 6.837 - Computer Graphics - Spring 2017 - MIT
- 6.838 - Shape Analysis - Spring 2017- MIT
- Introduction to Computer Graphics - IIT Delhi
- Computer Graphics - IIT Madras
- Computer Graphics 2012, Wolfgang Huerst, Utrecht University
- CS 5630/6630 - Visualization, Fall 2016, University of Utah (Lectures - Youtube)
- Advanced Visualization UC Davis
- Computer Graphics Fall 2011, Barbara Hecker
- Ray Tracing for Global Illumination, UCDavis
- Rendering / Ray Tracing Course, SS 2015 - TU Wien
- Computational Geometry - IIT Delhi
- CS 468 - Differential Geometry for Computer Science - Stanford University (Lecture videos)
- CMU 15-462/662: Computer Graphics
- UC Berkeley CS184/284A Computer Graphics and Imaging Spring 2022, by Ren Ng (YouTube playlist)
- CS 5610/6610 - Interactive Computer Graphics, Cem Yuksel - University of Utah (Lectures - YouTube)
- CS 15-458/858: Discrete Differential Geometry - Carnegie Mellon University - Spring 2021
- IN2124: Basic Mathematical Tools for Imaging and Visualization - TUM - Winter 2021
- KAIST CS580 Computer Graphics (Spring 2024)
- Discrete Differential Geometry - CMU 15-458/858
- Machine Learning For Inverse Graphics - MIT
- Geometric Structures - KAIST
- Digital Image Processing - IIT Kharagpur
- CS 543 - Computer Vision – Spring 2017 (Recordings)
- CAP 5415 - Computer Vision - University of Central Florida(Video Lectures)
- EE637 - Digital Image Processing I - Purdue University (Videos - Sp 2011,Videos - Sp 2007)
- Computer Vision I: Variational Methods - TU München (YouTube)
- Computer Vision II: Multiple View Geometry (IN2228), SS 2016 - TU München (YouTube)
- EENG 512/CSCI 512 - Computer Vision - Colorado School of Mines
- Computer Vision for Visual Effects - RPI (YouTube)
- Introduction to Image Processing - RPI (YouTube)
- CAP 6412 - Advanced Computer Vision - University of Central Florida(Video lectures) (Spring 2018)
- Digital Signal Processing - RPI
- UC Berkeley EE 123 Digital Signal Processing fall 2003, by Avideh Zakhor
- UC Berkeley EE 225B Digital Image Processing spring 2006, by Avideh Zakhor
- Gatech ECE 2026 Introduction to Signal Processing spring 2025, by Aaron Lanterman (YouTube)
- Gatech ECE 4270 Digital Signal Processing Fundamentals spring 2021, by David Anderson (YouTube)
- Gatech ECE 6250 Advanced Digital Signal Processing summer 2020, by David Anderson (YouTube)
- Gatech ECE 6271 Adaptive Signal Processing winter 2023, by David Anderson (YouTube)
- Advanced Vision 2014 - University of Edinburgh
- Photogrammetry Course - 2015/16 - University of Bonn, Germany
- MOOC - Introduction to Computer Vision - Udacity
- ECSE-4540 - Intro to Digital Image Processing - Spring 2015 - RPI
- Machine Learning for Computer Vision - Winter 2017-2018 - UniHeidelberg
- High-Level Vision - CBCSL OSU
- Advanced Computer Vision - CBCSL OSU
- Introduction to Image Processing & Computer Vision - CBCSL OSU
- Machine Learning for Computer Vision - TU Munich
- Biometrics - IIT Kanpur
- Quantitative Big Imaging 2019 ETH Zurich
- Multiple View Geometry in Computer Vision
- Modern C++ Course For CV (2020) - University of Bonn
- Photogrammetry 1 Course – 2020 - University of Bonn
- Photogrammetry II Course 2020/21 - University of Bonn
- 3D Computer Vision - National University of Singapore
- Diagnostic Medical Image Processing - Fall 2014 - FAU (Fall 2011) (Fall 2010) (Fall 2009)
- Interventional Medical Image Processing - Spring 2016 - FAU (Spring 2015) (Spring 2012) (Spring 2011) (Spring 2009)
- Advances in Computer Vision - MIT
- 3D Computer Vision | National University of Singapore
- Machine Learning for Computer Vision class (Winter 2017-2018) - UniHeidelberg
- Computer Vision Foundations Class (Summer 2020) - UniHeidelberg
- Statistics and Machine Learning for Astronomy
- Astronomical data analysis using Python 2021 - NRC IUCAA
- SPARC Workshop on Machine Learning in Solar Physics and Space Weather - CESSI IISER Kolkata
- Data-Driven Methods and Machine Learning in Atmospheric Sciences - IISC
- Computational Astrophysics - AstroTwinCoLo, 2015
- Astroinformatics 2019 Conference - Caltech
- Space Science with Python - Astroniz
- EE 210 Applied Electromagnetic Theory, UC Berkeley Videos - Fa 2025
- Computational Physics Course in Python, Rutgers 2021
- Landau Computational Physics Course
- Statistical Methods and Machine Learning in High Energy Physics
- Physics Informed Machine Learning by Steve Brunton
- Physics-informed machine learning meets engineering seminar series
- Physics Informed Machine Learning Workshop
- Jake VanderPlas: Machine learning in Astronomy python tutorial
- Machine Learning and Physics - UniHeidelberg
- ECS 124 - Foundations of Algorithms for Bioinformatics - Dan Gusfield, UC Davis (YouTube)
- CSE549 - Computational Biology - Steven Skiena - 2010 SBU
- 7.32 Systems Biology, Fall 2014 - MIT OCW
- 6.802J/ 6.874J Foundations of Computational and Systems Biology - MIT OCW
- 6.S897 Machine Learning For Healthcare
- 6.047/6.878 Machine Learning for Genomics Fall 2020 - MIT
- 6.874 MIT Deep Learning in Life Sciences - Spring 2021 - MIT
- 6.047/6.878 Public Lectures on Computational Biology: Genomes, Networks, Evolution - MIT
- Bio 84 - Your Genes and Your Health, Stanford University
- BioMedical Informatics 231 Computational Molecular Biology, Stanford University
- BioMedical Informatics 258 Genomics, Bioinformatics & Medicine, Stanford University
- 03-251: Introduction to Computational Molecular Biology - Carnegie Mellon University
- 03-712: Biological Modeling and Simulation - Carnegie Mellon University
- MOOC - Bioinformatics Algorithms: An Active Learning Approach - UC San Diego/Coursera
- Neural Networks and Biological Modeling - Lecturer: Prof. Wulfram Gerstner - EPFL
- Video Lectures of Wulfram Gerstner: Computational Neuroscience - EPFL
- An Introduction To Systems Biology
- Introduction to Bioinformatics, METUOpenCourseWare
- MOOC - Algorithms for DNA Sequencing, Coursera
- Frontiers of Biomedical Engineering with W. Mark Saltzman - Yale
- NOC:Computational Systems Biology - IIT Madras
- NOC:BioInformatics:Algorithms and Applications - IIT Madras
- MLCB24: Machine Learning in Computational Biology, Fall 2024 - Manolis Kellis - MIT
- Data Science and AI for Neuroscience Summer School - Caltech Neuroscience
- Theoretical and Computational Neuroscience Summer School - 2024 - CNeuro
- Neuroscience 299: Computing with High-Dimensional Vectors - Fall 2021 - UC Berkeley
- BIO410/510 Bioinformatics - California State University, Monterey Bay
- BIO412: Comparative Genomics - California State University, Monterey Bay
- CENG 465 - Introduction to Bioinformatics (Spring 2020-2021)
- UCLA Stats M254 Statistical Methods in Computational Biology spring 2024, by Jingyi Jessica Li
- Cell and Molecular Biology for Engineers ETH Zurich
- Statistical Models in Computational Biology
- ETH Zürich Statistical Models in Computational Biology spring 2018, by Niko Beerenwinkel
- UC Berkeley CS 198-96 Introduction to Neurotechnology fall 2020
- MLCB24 - Machine Learning in Computational Biology Fall 2024 (Fall 2018) (Fall 2019)
- Introduction to Neural Computation - MIT OCW
- Data Science for Biologists - Steve Brunton
- Big Data and Biological Networks IIT Madras
- Johns Hopkins Mathematical foundations of Biomedical Engineering, by Reza Shadmehr
- 15-859BB: Quantum Computation and Quantum Information 2018 - CMU (Youtube)
- Quantum Computation and Information at CMU
- Ph/CS 219A Quantum Computation - Prof Preskill - Caltech
- Quantum Mechanics and Quantum Computation - Umesh Vazirani
- Introduction to quantum computing course 2022 - NYU
- Phys 1470 - Foundations of Quantum Computing and Quantum Information - U of Pittsburgh
- Introduction to Quantum Computing From a Layperson to a Programmer in 30 Steps (EE225 SJSU)
- Quantum Computing Hardware and Architecture (EE274 SJSU)
- Quantum Physics for Non-Physicists 2021 - ETH Zurich (2020)
- Introduction to Quantum Computing and Quantum Hardware - Qiskit
- Understanding Quantum Information and Computation - Qiskit
- Lectures in Quantum Computation and Quantum Information (IIT Madras)
- Quantum Information and Computing by Prof. D.K. Ghosh
- Quantum Computing by Prof. Debabrata Goswami
- The Building Blocks of a Quantum Computer: Part 1 - TU Delft
- The Building Blocks of a Quantum Computer: Part 2 - TU Delft
- Quantum Cryptography - TU Delft
- Introduction to Quantum Information
- Quantum Computing for Everyone -- Part 1 (Part 2)
- Quantum Computer Systems – UChicago
- Quantum computing for the determined - Michael Nielsen
- Quantum Computing
- Advanced Topics in Quantum Info Th.
- Theory of Quantum Communication - University of Waterloo - Fall 2020
- Intro to Quantum Computing - Nathan Wiebe
- COMS 4281 - Introduction to Quantum Computing -Columbia University
- Introduction to Quantum Information Science (Online book, PDF)
- A practical introduction to Quantum Computing: from Qubits to Quantum Machine Learning: CERN
- PSI 2018/2019 - Quantum Information Review (Gottesman)
- ROB 101: Computational Linear Algebra - University of Michigan (Youtube - Fall 2021)
- ROB 102: Introduction to AI and Programming - University of Michigan
- Robotics 201: Calculus for the Modern Engineer - University of Michigan
- ROB 311: How to Build Robots and Make Them Move - University of Michigan
- ROB 320: Robot Operating Systems - University of Michigan
- ROB 501: Mathematics for Robotics - University of Michigan (Youtube)
- ROB 530 MOBILE ROBOTICS at U of Michigan - WINTER 2022 -- Instructor: Maani Ghaffari
- Autorob Winter 2022 - University of Michigan
- DeepRob Winter 2023 - University of Michigan
- CS 223A - Introduction to Robotics, Stanford University
- 6.832 Underactuated Robotics - MIT OCW
- 6.801 Machine Vision, MIT Fall 2020 - Berthold Horn (YouTube)
- CS287 Advanced Robotics at UC Berkeley Fall 2019 -- Instructor: Pieter Abbeel
- CS 287 - Advanced Robotics, Fall 2011, UC Berkeley (Videos)
- CMU 16-715 Robot Dynamics 2022 - CMU
- CMU 16-745 Optimal Control 2024 - CMU (Lecture notebooks) (YouTube-2023) (YouTube-2022)
- CMU 16-745 Optimal Control Recitations 2024 - CMU (YouTube-2023)
- CE 356 Elements of Hydraulic Engineering Spring 2025 - UT Austin
- CE 397 Control Theory for Smart Infrastructure Spring 2023 - UT Austin
- CS235 - Applied Robot Design for Non-Robot-Designers - Stanford University
- Lecture: Visual Navigation for Flying Robots (YouTube)
- CS 205A: Mathematical Methods for Robotics, Vision, and Graphics (Fall 2013)
- Optimization and Learning for Robot Control - University of Trento, Italy (YouTube-2025)
- Robotics 1, Prof. De Luca, Università di Roma (YouTube)
- Robotics 2, Prof. De Luca, Università di Roma (YouTube)
- Robot Mechanics and Control, SNU
- Introduction to Robotics Course - UNCC
- SLAM Lectures
- CSE 478 – Autonomous Robotics – Winter 2025 - University of Washington (Winter 2024)
- CSE 571 – AI-Robotics – Spring 2023 - University of Washington
- EE 259 – Principles of Sensing for Autonomy – Spring 2023 - Stanford University
- ME 597 – Autonomous Mobile Robotics – Fall 2014
- ME 780 – Perception For Autonomous Driving – Spring 2017
- ME780 – Nonlinear State Estimation for Robotics and Computer Vision – Spring 2017
- METR 4202/7202 -- Robotics & Automation - University of Queensland
- UIUC CS-588 Autonomous Vehicle System Engineering Outline
- Robotics - IIT Bombay
- Introduction to Machine Vision
- 6.834J Cognitive Robotics - MIT OCW
- Hello (Real) World with ROS – Robot Operating System - TU Delft
- Programming for Robotics (ROS) - ETH Zurich
- Mechatronic System Design - TU Delft
- CS 206 Evolutionary Robotics Course Spring 2020
- Foundations of Robotics - UTEC 2018-I
- Robotics and Control: Theory and Practice IIT Roorkee
- Mechatronics
- ME142 - Mechatronics Spring 2020 - UC Merced
- Mobile Sensing and Robotics - Bonn University
- MSR2 - Sensors and State Estimation Course (2020) - Bonn University
- SLAM Course (2013) - Bonn University
- ENGR486 Robot Modeling and Control (2014W)
- Robotics by Prof. D K Pratihar - IIT Kharagpur
- Introduction to Mobile Robotics - SS 2019 - Universität Freiburg
- Robot Mapping - WS 2018/19 - Universität Freiburg
- Mechanism and Robot Kinematics - IIT Kharagpur
- Self-Driving Cars - Cyrill Stachniss - Winter 2020/21 - University of Bonn)
- Aerial Robotics - University of Pennsylvania (UPenn)
- Modern Robotics - Northwestern University
- MIT 6.4210/6.4212 - Robotic Manipulation - MIT (Youtube)
- Industrial Robotics and Automation - IIT (ISM) Dhanbad
- MEE5114 Advanced Control for Robotics from Southern University of Science and Technology
- Self-Driving Cars — Andreas Geiger
- Signal Processing: An Introduction by Nathan Kutz
- UC Santa Barbara ME 269 Network Systems, Dynamics and Control fall 2021, by Francesco Bullo
- Cornell MAE 4710/5710 Applied Dynamics spring 2020, by Andy Ruina (Part 2)
- Cornell MAE 4730/5730 Intermediate Dynamics fall 2020, by Andy Ruina
- CMU 16 299 Introduction to Feedback Control Systems spring 2022, by Chris Atkeson
- University of Wisconsin-Madison ECE 332 Feedback Control Systems fall 2021, by Steven Fredette
- MAE 509 Linear Matrix Inequality Methods in Optimal and Robust Control, by Matthew M. Peet
- UIUC CS 588 Autonomous Vehicle System Engineering fall 2021, by David Forsyth
- UCCS ECE4590/ECE5590 Model Predictive Control, by M. Scott Trimboli
- EPFL ME 425 Model Predictive Control fall 2020, by Colin Jones
- Robots That Learn - UC Berkeley CS 294-277
- Data-Driven Dynamical Systems with Machine Learning - Steve Brunton
- Data-Driven Control with Machine Learning - Steve Brunton
- Wheeled Mobile Robots
- Introduction to Mobile Robots and Robot Operating System (ROS), HSE 2021
- Surgical Robotics Lectures - Carleton University
- Introduction to Mechatronic and Robotics - IIT Bombay
- Robotics Fall 2020 - UIC
- Introduction to Robotics @ Princeton (Slides)
- Evolutionary robotics course. Spring 2025
- Robotics: Basics and Selected Advanced - IISC Bangalore
- Nonlinear Control Design Jan 2024 - IIT Bombay
- Intelligent Control of Robotic Systems - IIT Roorkee
- Mobile Robot Systems Course - Amanda Prorok, University of Cambridge
- Robot Academy - Peter Corke
- ENAE 788M: Hands On Autonomous Aerial Robotics - University of Maryland
- Robot Learning 2025: Foundational Models for Robotics and Scaling DeepRL - Montreal Robotics
- Mila Robot Learning Seminar - Montreal Robotics
- Johns Hopkins Learning, Estimation, and Control spring 2026, by Reza ShadmehrYoutube
- SUSTech ME424 Modern Control and Estimation ( Kalman Filter Theory ) fall 2021, by Wei ZhangYoutube 1Youtube 2
- Robot Learning - ETH Zurich (Youtube, Guest lectures)
- COMP510 - Computational Finance - Steven Skiena - 2007 HKUST
- Computational Finance Course - Prof Grzelak
- Financial Engineering Course: Interest Rates and xVA - Prof Grzelak
- MOOC - Mathematical Methods for Quantitative Finance, University of Washington/Coursera)
- 18.S096 Topics in Mathematics with Applications in Finance, Fall 2013 MIT OCW
- 18.642 Topics in Mathematics with Applications in Finance, Fall 2024 MIT OCW
- Computational Finance - Universität Leipzig
- Machine Learning for Trading | Udacity
- ACT 460 / STA 2502 – Stochastic Methods for Actuarial Science - University of Toronto
- MMF1928H / STA 2503F – Pricing Theory I / Applied Probability for Mathematical Finance - University of Toronto
- STA 4505H – High Frequency & Algorithmic trading - University of Toronto
- Mathematical Finance - IIT Guwahati
- Quantitative Finance - IIT Kanpur
- Financial Derivatives & Risk Management - IIT Roorkee
- Financial Mathematics - IIT Roorkee
- Harvard Economics 2355 Deep Learning for Economics spring 2023, by Melissa Dell
- UW ECON 484 Econometrics and Data Science spring 2020, by Gregory Duncan
- MATH69122 Stochastic Control for Finance
- UC Davis MAT 133 Mathematical Finance Spring 2024, by Matthias Köppe (Spring 2021)
- Network Science, 2021 - HSE
- Network Science TU Graz
- MATH/COMP 479 Network Science Macalester College
- ACM Winter School on Network Science _Dec 2023, Ahmedabad University
- Network Science 2021/2022 (ENS Lyon)
- Blockchain and Cryptocurrencies
- Blockchain, Solidity, and Full Stack Web3 Development with JavaScript
- Blockchain Fundamentals Decal 2018 - Berkeley DeCal
- Blockchain for Developers Decal - Spring 2018 - Berkeley DeCal
- Cryptocurrency Engineering and Design - Spring 2018 - MIT
- 15.S12 Blockchain and Money, Fall 2018 - MIT
- Bitcoin and Cryptocurrency Technologies - Arvind Narayanan, Princeton University (YouTube Lectures) (Free book PDF)
- Blockchain - Foundations and Use Cases
- Become Blockchain Developer
- HCI
- Game Development
- CS50's Introduction to Game Development
- MIT CMS.611J Creating Video Games, Fall 2014
- MOOC - Beginning Game Programming with C# - Coursera
- Gatech ECE4795 GPU Programming for Video Games, Summer 2021
- COMP 4300 - C++ Game Programming, Memorial University, Fall 2024 - David Churchill
- COMP 4303 - AI for Video Games, Memorial University, Winter 2023 - David Churchill
- Geospatial
- MOOC - Matlab - Coursera
- Computing for Computer Scientists - University of Michigan
- Linux System Administration Decal, Spring 2025, UC Berkeley
- Linux Implementation/Administration Practicum - Redhat by Tulio Llosa
- Innovative Computing - Harvard University
- Linux Programming & Scripting - IIT Madras
- Model Checking - IIT Madras
- Virtual Reality - IIT Madras
- Dependable Systems (SS 2014) - HPI University of Potsdam
- Business Process Compliance (WT 2013/14) - HPI University of Potsdam
- Design Thinking for Digital Engineering (SS 2018) - Dr. Julia von Thienen - HPI
- CS224w – Social Network Analysis – Autumn 2017 - Stanford University
- The Missing Semester of Your CS Education
- University of Crete, Computer Science video lectures (mostly Greek language lectures, very few 100% English-speaking courses). Very popular CS destination for European Erasmus students
- Stanford EE274 I Data Compression: Theory and Applications I 2023
- Probabilistic Methods - University of Waterloo
- Free Probability Theory and Ramanujan Graphs - Spring 2024
- Asymptotics and perturbation methods - Prof. Steven Strogatz
- ETH Zürich AI in the Sciences and Engineering
- Introduction to GIS Programming (Fall 2024) - Open Geospatial Solutions
- Gatech Guitar Amplification and Effects, by Aaron Lanterman
- Gatech ECE3084 Signals and Systems summer 2020, by Aaron Lanterman
- Gatech ECE4450 Analog Circuits for Music Synthesis spring 2021, by Aaron Lanterman
- UC Berkeley EE 120 Signals and Systems spring 2019, by Murat Arcak
- Stanford EE 102 spring 1999, Introduction to Signals and Systems, by Stephen Boyd
- Stanford EE 376a winter 2011, Information Theory, by Thomas Cover
- MIT RES.6.007 Signals and Systems, 1987 - MIT
- MIT 9.19 Computational Psycholinguistics, 2023 - MIT (YouTube)
- MIT 21M.383 Computational Music Theory and Analysis, Spring 2023 - MIT (YouTube)
- UCCS ECE4510/ECE5510 Feedback Control Systems, by Gregory Plett
- UCCS ECE4520/ECE5520 Multivariable Control Systems I, by Gregory Plett
- UCCS ECE4530/ECE5530 Multivariable Control Systems II, by Gregory Plett
- UCCS ECE4540/ECE5540 Digital Control Systems, by Gregory Plett
- UCCS ECE4550/ECE5550 Applied Kalman Filtering, by Gregory Plett
- UCCS ECE4560/ECE5560 System Identification, by Gregory Plett
- UCCS ECE4570/ECE5570 Optimization for Systems and Control, by M. Scott Trimboli
- UCCS ECE4580/ECE5580 Multivariable Control in the Frequency Domain, by M. Scott Trimboli
- UCCS ECE4710/ECE5710 Modeling, Simulation, and Identification of Battery Dynamics, by Gregory Plett
- UCCS ECE4720/ECE5720 Battery Management and Control, by Gregory Plett
- Purdue ME 597 Distributed Energy Resources Spring 2024, by Kevin J. Kircher
- Stanford AA228V/CS238V Validation of Safety Critical Systems Winter 2025, by Sydney Michelle Katz
- RES.1-002 - Introduction to R and Geographic Information Systems (GIS) - MIT - Fall 2023
- CMU 17 803 Empirical Methods spring 2026, by Bogdan VasilescuYoutube
Repository Radar analysis
Deterministic insights derived from public metadata and our observations — not personal testing or reviews.
Why this repository is interesting
- High absolute popularity (83,391 stars) signals broad adoption.
- Maintained recently (last push 3 weeks ago).
Who should use it
- Teams exploring AI tooling, agents, or ML infrastructure
- Platform and DevOps engineers
- Security-minded engineers reviewing tooling options
Potential use cases
- Prototype AI/agent workflows or study reference architectures
- Evaluate automation, CI/CD, or infrastructure patterns
Strengths
- Recent repository activity
- README present in our index
- Substantial fork count (11,477) suggests reuse and contribution interest
Limitations / considerations
- License not recorded in our index — confirm on GitHub before production use
What to watch
- Re-check last push, issues, and releases on GitHub before production adoption
Strong signals: Strong community interest · Active maintenance
Source: GitHub (public metadata) + Repository Radar analysis. We do not claim ownership of third-party repositories.