Computer Science courses with video lectures
Introduction
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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
Courses
Introduction to Computer Science
- 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
Data Structures and Algorithms
- 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
Systems Programming
- 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
-
Operating Systems
- 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)
-
Distributed Systems
- 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
-
Real-Time Systems
- 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)
Database Systems
- 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
Software Engineering
-
Object Oriented Design
- 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
-
Software Engineering
- 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
-
Software Architecture
-
Concurrency
- 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
-
Mobile Application Development
- 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
Artificial Intelligence
- 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
Machine Learning
-
Introduction to Machine Learning
- 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) (Spring 2022) (Spring 2026)
- 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
-
Data Mining
- 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)
- DSC 253/CSE 261 - Advanced Data-Driven Text Mining, Winter 2026, UC San Diego by Prof. Jingbo Shang
- 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)
- DSC 148 - Introduction to Data Mining - UCSD
- CS 412 - Introduction to Data Mining - UIUC
- CS 512 - Data Mining Principles - UIUC (YouTube)
- MGTA 415 - Analyzing Unstructured Data - UCSD
-
Probabilistic Graphical Modeling
-
Deep Learning
- 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
- 6.S985: How to AI Almost Anything/Multimodal AI - 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
-
Reinforcement Learning
- 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
- 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
- Introduction to High Dimensional Statistics and Probability, by Christophe Giraud (Lecture videos)
- 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
-
Natural Language Processing
- 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)
-
Generative AI and LLMs
- 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 CS329A Self-Improving AI Agents 2025 - Stanford (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
-
Computer Vision
- 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)
-
Time Series Analysis
-
Optimization
- 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
-
Unsupervised Learning
- 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
-
Misc Machine Learning Topics
- 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