Best collection of 500+ Computer Science courses with Videos and Books
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This post is for sharing amazing Computer Science resources . Hope it makes your CS journey more exciting and focussed towards your area of interest.
Python
- Think Python: How does one Think Like a Computer Scientist Allen Downey
- Python Programming Wikibooks
- Automate the Boring Stuff with Python: Practical Programming for Total Beginners Al Sweigart
- Learn Python the Hard Way Zed A. Shaw
- Dive Into Python 3 Mark Pilgrim
- Full Stack Python — Matt Makai
- Functional Programming in Python — David Mertz
- Fundamentals of Python Programming — Richard L. Halterman (PDF)
- Google’s Python Class
- Google’s Python Style Guide
- Hadoop with Python — Zachary Radtka, Donald Miner
- Hitchhiker’s Guide to Python!
- How to Code in Python 3 — Lisa Tagliaferri (PDF)
- How to Make Mistakes in Python — Mike Pirnat (PDF) (1st edition)
Projects Videos —
All the projects, data structures, SQL, algorithms, system design, Data Science and ML , Data Analytics, Data Engineering, , Implemented Data Science and ML projects, Implemented Data Engineering Projects, Implemented Deep Learning Projects, Implemented Machine Learning Ops Projects, Implemented Time Series Analysis and Forecasting Projects, Implemented Applied Machine Learning Projects, Implemented Tensorflow and Keras Projects, Implemented PyTorch Projects, Implemented Scikit Learn Projects, Implemented Big Data Projects, Implemented Cloud Machine Learning Projects, Implemented Neural Networks Projects, Implemented OpenCV Projects,Complete ML Research Papers Summarized, Implemented Data Analytics projects, Implemented Data Visualization Projects, Implemented Data Mining Projects, Implemented Natural Leaning Processing Projects, MLOps and Deep Learning, Applied Machine Learning with Projects Series, PyTorch with Projects Series, Tensorflow and Keras with Projects Series, Scikit Learn Series with Projects, Time Series Analysis and Forecasting with Projects Series, ML System Design Case Studies Series videos will be published on our youtube channel ( just launched).
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- How to Think Like a Computer Scientist: Learning with Python, Interactive Edition — Brad Miller, David Ranum, Jeffrey Elkner, Peter Wentworth, Allen B. Downey, Chris Meyers, and Dario Mitchell (3.2)
- How to Think Like a Computer Scientist: Learning with Python 1st Edition — Allen B. Downey, Jeff Elkner and Chris Meyers (2.4) (HTML, PDF)
- How to Think Like a Computer Scientist: Learning with Python 2nd Edition — Jeffrey Elkner, Allen B. Downey, and Chris Meyers (Using Python 2.x)
- How to Think Like a Computer Scientist: Learning with Python 3 (RLE) — Peter Wentworth, Jeffrey Elkner, Allen B. Downey, and Chris Meyers (PDF)
- Intermediate Python — Muhammad Yasoob Ullah Khalid (1st edition)
- Introduction to Programming with Python (3.3)
- Introduction to Programming Using Python (2.3)
- Introduction to Python — Kracekumar (2.7.3)
- Getting Started With Python In The Lab: An Introductory Python Tutorial For Life Scientists
- Python Data Science Handbook
- Clean Architectures in Python — Leonardo Giordani
- Code Like a Pythonista: Idiomatic Python — David Goodger
- CodeCademy Python
- Composing Programs (3.x)
- Cracking Codes with Python — Al Sweigart (3.6)
- Data Structures and Algorithms in Python — B. R. Preiss (PDF)
- Dive into Python 3 — Mark Pilgrim (3.0)
- Dive into Python — Mark Pilgrim (2.3)
- From Python to NumPy — Nicolas P. Rougier (3.6)
- Modeling and Simulation in Python — Allen B. Downey (PDF)
- Modeling Creativity: Case Studies in Python — Tom D. De Smedt (PDF)
- Natural Language Processing (NLP) with Python — Tutorial (PDF)
- Natural Language Processing with Python (3.x)
- Non-Programmer’s Tutorial for Python 3 — Wikibooks (3.3)
- Non-Programmer’s Tutorial for Python 2.6 — Wikibooks (2.6)
- Picking a Python Version: A Manifesto — David Mertz
- Porting to Python 3: An In-Depth Guide (2.6–2.x & 3.1–3.x)
- Practical Programming in Python — Jeffrey Elkner (PDF)
- Teaching and Learning with Jupyter
- Python Tutorial — Tutorials Point
- Scipy Lecture Notes
- SICP in Python (3.2)
- Slither into Python: An introduction to Python for beginners (3.7)
- Suporting Python 3: An In-Depth Guide
- Test-Driven Web Development with Python — Harry Percival (3.3–3.x)
- Text Processing in Python — David Mertz (2.3–2.x)
- The Coder’s Apprentice: Learning Programming with Python 3 — Pieter Spronck (PDF) (3.x)
- The Definitive Guide to Jython, Python for the Java Platform — Josh Juneau, Jim Baker, Victor Ng, Leo Soto, Frank Wierzbicki (2.5)
- The Little Book of Python Anti-Patterns (Source)
- The Programming Historian — William J. Turkel, Adam Crymble and Alan MacEachern
- The Python GTK+ 3 Tutorial
- The Standard Python Library — Fredrik Lundh
- Think Complexity — Allen B. Downey (2nd Edition) (PDF, HTML)
- Think DSP — Digital Signal Processing in Python — Allen B. Downey (PDF, HTML)
- Think Python 2nd Edition — Allen B. Downey (3.x) (HTML, PDF)
- Think Python First Edition — Allen B. Downey (2.x) (HTML, PDF)
- PySDR: A Guide to SDR and DSP using Python
- Python for network engineers
- Cracking Codes with Python
- Coding Games With Pygame Zero & Python
- Invent Your Own Computer Games with Python
- Learn Python With Arcade Academy
- Making Games with Python & Pygame
- Program Arcade Games With Python And Pygame
- Boxes: Your Second Python Book
- Intermediate Python
- Python re(gex)?
- Python Tips: Intermediate Python
- A Python Book: Beginning Python, Advanced Python, and Python Ex
- Python Notes for Professionals
- Machine Learning from Scratch: Derivations in Concept and Code
- Natural Language Processing with Python
- Problem Solving with Algorithms and Data Structures using Python — Bradley N. Miller and David L. Ranum (3.x)
- Program Arcade Games With Python And Pygame (3.3)
- Programming Computer Vision with Python (PDF)
- Programming for Non-Programmers, Release 2.6.2 — Steven F. Lott (PDF) (2.6)
- PySDR: A Guide to SDR and DSP using Python — Dr. Marc Lichtman (3.x)
- Python 101 — Michael Driscoll (HTML, TEXT)
- Python 2 Official Documentation (PDF, HTML, TEXT) (2.x)
- Python 2.7 quick reference — John W. Shipman (PDF) (2.7)
- Python 3 Official Documentation (PDF, EPUB, HTML, TEXT) (3.x)
- Python 3 Tutorial
- Python Data Science Handbook — Jake VanderPlas (HTML, Jupyter Notebooks)
- Python for Everybody — Charles Russell Severance (PDF, EPUB, HTML) (3.x)
- Python for Informatics: Exploring Information — Charles Russell Severance (2.7.5)
- Python for you and me (2.7.3)
- Python for you and me (3.x)
- Python Idioms (PDF)
- Python in Education — Nicholas Tollervey
- Python in Hydrology — Sat Kumar Tomer
- Python Koans (2.7 or 3.x)
- Python Machine Learning By Example —
- Python Module of the Week (3.x)
- Python Module of the Week (2.x)
- Python Notes for Professionals — Compiled from StackOverflow documentation (PDF)
- Python Practice Book (2.7.1)
- Python Practice Projects
- Python Programming — Wikibooks (2.7)
- Python Programming — Wikibooks (PDF) (2.6)
- Python Programming And Numerical Methods: A Guide For Engineers And Scientists — Qingkai Kong, Timmy Siauw, Alexandre Bayen (3.7)
- Neural Networks and Deep Learning
- Probabilistic Programming & Bayesian Methods for Hackers
- Annotated Algorithms in Python — With applications in Physics, Biology, and Finance
- How to Think Like a Computer Scientist: Interactive Edition
- Problem Solving with Algorithms and Data Structures using Python
- Think Python: How to Think Like a Computer Scientist
- Architecture Patterns with Python: Enabling Test-Driven Development, Domain-Driven Design, and Event-Driven Microservices
- Clean Architectures in Python: A practical approach to better software design
- Object Oriented Programming with Python: Learn essentials of OOP with Python 3
- Test-Driven Development with Python
- The Hitchhiker’s Guide to Python!
- The Little Book of Python Anti-Patterns
- Create Simple GUI Applications
- Automate the Boring Stuff with Python
R programming language
- R Programming for Data Science Roger D. Peng
- R Programming Wikibooks
- Advanced R Hadley Wickham
SQL
- Learn SQL The Hard Way Zed. A. Shaw
Statistics and Statistical Learning
- Think Stats: Exploratory Data Analysis in Python Allen B. Downey
- Think Bayes: Bayesian Statistics Made Simple Allen B. Downey
- An Introduction to Statistical Learning with Applications in R Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani
- A First Course in Design and Analysis of Experiments Gary W. Oehlert
- Applied Data Science — Ian Langmore and Daniel Krasner
- Concepts and Applications of Inferential Statistics — Richard Lowry
- Forecasting: Principles and Practice — Rob J. Hyndman and George Athanasopoulos
- Statistics
- Computer Age Statistical Inference: Algorithms, Evidence and Data Science — Bradley Efron and Trevor Hastie
- Introduction to Probability — Charles M. Grinstead and J. Laurie Snell
Data Science in General
- An Introduction to Data Science Jeffrey Stanton
- School of Data Handbook School of Data
- Data Jujitsu: The Art of Turning Data into Product DJ Patil
Data Processing
- Data-Intensive Text Processing with MapReduce — Jimmy Lin and Chris Dyer
Data Analysis
- The Elements of Data Analytic Style Jeff Leek
- Fundamental Numerical Methods and Data Analysis — George W. Collins
- Introduction to R — Notes on R: A Programming Environment for Data Analysis and Graphics — W. N. Venables, D. M. Smith, and the R Core Team
- Modeling with Data: Tools and Techniques for Scientific Computing — Ben Klemens
- R for Data Science: Import, Tidy, Transform, Visualize, and Model Data — Hadley Wickham & Garrett Grolemund
- Advanced R — Hadley Wickham
Data Mining and Machine Learning
- Introduction to Machine Learning Amnon Shashua
- Machine Learning Abdelhamid Mellouk & Abdennacer Chebira,
- Machine Learning — The Complete Guide Wikipedia
- Social Media Mining An Introduction Reza Zafarani, Mohammad Ali Abbasi, & Huan Liu
- Mining of Massive Datasets Jure Leskovec, Anand Rajaraman, & Jeff Ullman
- A Programmer’s Guide to Data Mining Ron Zacharski
- Probabilistic Programming & Bayesian Methods for Hackers Cam Davidson-Pilon
- Machine Learning, Neural and Statistical Classification D. Michie, D.J. Spiegelhalter, & C.C. Taylor
- Information Theory, Inference, and Learning Algorithms David J.C. MacKay
- Bayesian Reasoning and Machine Learning David Barber
- Gaussian Processes for Machine Learning C. E. Rasmussen & C. K. I. Williams
- Reinforcement Learning: An Introduction Richard S. Sutton & Andrew G. Barto
- Algorithms for Reinforcement Learning Csaba Szepesvari
- Modeling With Data Ben Klemens
- KB — Neural Data Mining with Python Sources Roberto Bello
- Neural Networks and Deep Learning Michael Nielsen
- Data Mining Algorithms In R Wikibooks
- Data Mining and Analysis: Fundamental Concepts and Algorithms Mohammed J. Zaki & Wagner Meira Jr.
- Theory and Applications for Advanced Text Mining Shigeaki Sakurai
Information Retrieval
- Introduction to Information Retrieval — Christopher D. Manning, Prabhakar Raghavan, and Hinrich Schutze
Data Visualization
- D3 Tips and Tricks Malcolm Maclean
- Plotting and Visualization in Python
- ggplot2: Elegant Graphics for Data Analysis — Hadley Wickham
Interviews with Data Scientists
- The Data Science Handbook Carl Shan, Henry Wang, William Chen, & Max Song
Forming Data Science Teams
- Data-Driven: Creating a Data Culture Hilary Mason & DJ Patil
- Building Data Science Teams DJ Patil
- Understanding the Chief Data Officer Julie Steele
Distributed Computing Tools
- Hadoop: The Definitive Guide Tom White
- Data-Intensive Text Processing with MapReduce Jimmy Lin & Chris Dyer
Big Data
- Big Data Now: 2012 Edition O’Reilly Media, Inc.
- The Promise and Peril of Big Data — David Bollier
CS courses with Video Lectures —
Introduction to Computer Science
- 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
- CS 50 — Introduction to Computer Science, Harvard University (cs50.tv)
- 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 (Lecture Videos)
- CS 106X — Programming Abstractions in C++ (Lecture Videos)
- CS 107 — Programming Paradigms, Stanford University
- CmSc 150 — Introduction to Programming with Arcade Games, Simpson College
- 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
Data Structures and Algorithms
- 6.006 — Introduction to Algorithms, MIT OCW
- 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 — Richard Buckland — 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
- 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 170 Algorithms — UCBerkeley Fall 2018, Youtube Fall 2018,Bilibili 2013 Bilibili
- ECS 122A — Algorithm Design and Analysis, UC Davis
- 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)
- Informatics 2B — Algorithms, Data Structures and Learning- University of Edinburgh
- 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
- 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
Systems Programming
- 15–213 Introduction to Computer Systems, Fall 2015 — CMU
- CS361 — COMPUTER SYSTEMS — UIC
- CS 4400 — Computer Systems Fall 2016 — UoUtah
- Systems — Aduni
- CS110: Principles of Computer Systems — Stanford
- Operating Systems
- CS124 Operating Systems — California Institute of Technology, Fall 2018 — Youtube
- CS 162 Operating Systems and Systems Programming, Spring 2015 — University of California, Berkeley
- 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
- 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
Distributed Systems
- CS 677 — Distributed Operating Systems, Spring 16 — 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 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
- Real-Time Systems
- CPCS 663 — Real-Time Systems: Video Material — TAMU
- Real Time Systems — IIT Kharagpur
- 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 Colloquim 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-2018, YouTube-2017, YouTube-2019)
- 15–721 — Database Systems, CMU (YouTube-2017, YouTube-2016)
- 15–721 Advanced Database Systems (Spring 2019) — CMU
- CS122 — Relational Database System Implementation, Winter 2014–2015 — Caltech
- CS 186 — Database Systems, UC Berkeley, Spring 2015 (Lectures- InfoCoBuild)
- 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
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
- Computer Science 169- Software Engineering — Spring 2015 — 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
- Software Testing — IIT Kharagpur
- Software Testing — Udacity, course-cs258 | 2015
- Software Debugging — Udacity, course-cs259 | 2015
- Software Engineering — Bauhaus-Uni Weimar
Software Architecture
- CS 411 — Software Architecture Design — Bilkent University
- MOOC — Software Architecture & Design — Udacity
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
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
- CS 188 — Introduction to Artificial Intelligence, UC Berkeley — Spring 2015
- 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 4804: Introduction to Artificial Intelligence, Fall 2016
- CS 5804: Introduction to Artificial Intelligence, Spring 2015
- 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
Machine Learning
Introduction to Machine Learning
- MOOC Machine Learning Andrew Ng — Coursera/Stanford (Notes)
- Introduction to Machine Learning for Coders
- MOOC — Statistical Learning, Stanford University
- Foundations of Machine Learning Boot Camp, Berkeley Simons Institute
- CS155 — Machine Learning & Data Mining, 2017 — Caltech (Notes) (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)
- 10–301/601 — Introduction to Machine Learning — Spring 2020 — CMU
- CMS 165 Foundations of Machine Learning and Statistical Inference — 2020 — Caltech
- Microsoft Research — Machine Learning Course
- CS 446 — Machine Learning, Spring 2019, UIUC( Fall 2016 Lectures)
- undergraduate machine learning at UBC 2012, Nando de Freitas
- CS 229 — Machine Learning — Stanford University (Autumn 2018)
- CS 189/289A Introduction to Machine Learning, Prof Jonathan Shewchuk — UCBerkeley
- CPSC 340: Machine Learning and Data Mining (2018) — UBC
- 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, Fall 2016, University of Utah
- 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
- 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
- Python and machine learning — Stanford Crowd Course Initiative
- MOOC — Machine Learning Part 1a — Udacity/Georgia Tech (Part 1b Part 2 Part 3)
- Machine Learning and Pattern Recognition 2015/16- University of Edinburgh
- Introductory Applied Machine Learning 2015/16- University of Edinburgh
- 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
- 10715 Advanced Introduction to Machine Learning
- Introduction to Machine Learning — Spring 2018 — ETH Zurich
- Machine Learning — Pedro Domingos- University of Washington
- Advanced Machine Learning — 2019 — ETH Zürich
- 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
Data Mining
- CSEP 546, Data Mining — Pedro Domingos, Sp 2016 — University of Washington (YouTube)
- CS 5140/6140 — Data Mining, Spring 2016, University of Utah (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)
- Data Mining: Learning From Large Datasets — Fall 2017 — ETH Zurich
- Information Retrieval — Spring 2018 — ETH Zurich
- CAP6673 — Data Mining and Machine Learning — FAU(Video lectures)
- Data Warehousing and Data Mining Techniques — Technische Universität Braunschweig, Germany
Data Science
- Data 8: The Foundations of Data Science — UC Berkeley (Summer 17)
- 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 — Summer 19- UC Berkeley
- Distributed Data Analytics (WT 2017/18) — HPI University of Potsdam
- Statistics 133 — Concepts in Computing with Data, Fall 2013 — UC Berkeley
- Data Profiling and Data Cleansing (WS 2014/15) — HPI University of Potsdam
- AM 207 — Stochastic Methods for Data Analysis, Inference and Optimization, Harvard University
- CS 229r — Algorithms for Big Data, Harvard University (Youtube)
- Algorithms for Big Data — IIT Madras
Probabilistic Graphical Modeling
- MOOC — Probabilistic Graphical Models — Coursera
- CS 6190 — Probabilistic Modeling, Spring 2016, University of Utah
- 10–708 — Probabilistic Graphical Models, Carnegie Mellon University
- Probabilistic Graphical Models, Daphne Koller, Stanford University
- Probabilistic Models — UNIVERSITY OF HELSINKI
- Probabilistic Modelling and Reasoning 2015/16- University of Edinburgh
- Probabilistic Graphical Models, Spring 2018 — Notre Dame
Deep Learning
- 6.S191: Introduction to Deep Learning — MIT
- Deep Learning CMU
- 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
- 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
- STAT-157 Deep Learning 2019 — UC Berkeley
- Full Stack DL Bootcamp 2019 — UC Berkeley
- Deep Learning, Stanford University
- MOOC — Neural Networks for Machine Learning, Geoffrey Hinton 2016 — Coursera
- Deep Unsupervised Learning — Berkeley Spring 2020
- Stat 946 Deep Learning — University of Waterloo
- Neural networks class — Université de Sherbrooke (YouTube)
- CS294–158 Deep Unsupervised Learning SP19
- 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
- Nvidia Machine Learning Class
- Deep Learning — Winter 2020–21 — Tübingen Machine Learning
Reinforcement Learning
- CS234: Reinforcement Learning — Winter 2019 — Stanford University
- Introduction to reinforcement learning — UCL
- Advanced Deep Learning & Reinforcement Learning — UCL
- Reinforcement Learning — IIT Madras
- CS885 Reinforcement Learning — Spring 2018 — University of Waterloo
- CS 285 — Deep Reinforcement Learning- UC Berkeley
- 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)
- Deep RL Bootcamp — Berkeley Aug 2017
- Reinforcement Learning — IIT Madras
Advanced Machine Learning
- 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
- 18.409 Algorithmic Aspects of Machine Learning Spring 2015 — MIT
- CS 330 — Deep Multi-Task and Meta Learning — Fall 2019 — Stanford University (Youtube)
ML based Natural Language Processing and Computer Vision
- CS 224d — Deep Learning for Natural Language Processing, Stanford University (Lectures — Youtube)
- CS 224N — Natural Language Processing, Stanford University (Lecture videos)
- CS 124 — From Languages to Information — Stanford University
- MOOC — Natural Language Processing, Dan Jurafsky & Chris Manning — Coursera
- fast.ai Code-First Intro to Natural Language Processing (Github)
- MOOC — Natural Language Processing — Coursera, University of Michigan
- CS 231n — Convolutional Neural Networks for Visual Recognition, Stanford University
- CS224U: Natural Language Understanding — Spring 2019 — Stanford University
- Deep Learning for Natural Language Processing, 2017 — Oxford University
- Machine Learning for Robotics and Computer Vision, WS 2013/2014 — TU München (YouTube)
- Informatics 1 — Cognitive Science 2015/16- University of Edinburgh
- Informatics 2A — Processing Formal and Natural Languages 2016–17 — University of Edinburgh
- Computational Cognitive Science 2015/16- University of Edinburgh
- Accelerated Natural Language Processing 2015/16- University of Edinburgh
- Natural Language Processing — IIT Bombay
- NOC:Deep Learning For Visual Computing — IIT Kharagpur
- CS 11–747 — Neural Nets for NLP — 2019 — CMU
- Natural Language Processing — Michael Collins — Columbia University
- Deep Learning for Computer Vision — University of Michigan
- CMU CS11–737 — Multilingual Natural Language Processing
Time Series Analysis
Misc Machine Learning Topics
- EE364a: Convex Optimization I — Stanford University
- CS 6955 — Clustering, Spring 2015, University of Utah
- Info 290 — Analyzing Big Data with Twitter, UC Berkeley school of information (YouTube)
- 10–725 Convex Optimization, Spring 2015 — CMU
- 10–725 Convex Optimization: Fall 2016 — CMU
- CAM 383M — Statistical and Discrete Methods for Scientific Computing, University of Texas
- 9.520 — Statistical Learning Theory and Applications, Fall 2015 — MIT
- Reinforcement Learning — UCL
- Regularization Methods for Machine Learning 2016 (YouTube)
- Statistical Inference in Big Data — University of Toronto
- 10–725 Optimization Fall 2012 — CMU
- 10–801 Advanced Optimization and Randomized Methods — CMU (YouTube)
- Reinforcement Learning 2015/16- University of Edinburgh
- Reinforcement Learning — IIT Madras
- Statistical Rethinking Winter 2015 — Richard McElreath
- Music Information Retrieval — University of Victoria, 2014
- PURDUE Machine Learning Summer School 2011
- Foundations of Machine Learning — Blmmoberg Edu
- Introduction to reinforcement learning — UCL
- Advanced Deep Learning & Reinforcement Learning — UCL
- 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
Computer Networks
- 14–740 — Fundamentals of Computer Networks — CMU
- CS 144 Introduction to Computer Networking — Stanford University, Fall 2013 (Lecture videos)
- 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)
- Computer Networks — IIT Kharagpur
- Introduction to Data Communications 2013, Steven Gordon — Thammasat University, Thailand
- Introduction to Complex Networks — RIT
- Structural Analysis and Visualization of Networks
- 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
Math for Computer Scientist
Calculus
- 18.01 Single Variable Calculus, Fall 2006 — MIT OCW
- 18.02 Multivariable Calculus, Fall 2007 — MIT OCW
- 18.03 Differential Equations, Spring 2010 — MIT OCW
- Highlights of Calculus — Gilbert Strang, MIT OCW
Discrete Math
- 6.042J — Mathematics for Computer Science, Fall 2010, MIT OCW (Spring 15)
- Computer Science 70, 001 — Spring 2015
- 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
Probability & Statistics
- 6.041 Probabilistic Systems Analysis and Applied Probability — MIT OCW
- Statistics 110 — Probability — Harvard University
- 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 — Aalto University) (Book)
- 02402 Introduction to Statistics E12 — Technical University of Denmark (F17)
Linear Algebra
- 18.06 — Linear Algebra, Prof. Gilbert Strang, MIT OCW
- 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning — MIT OCW
- 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)
- 10–600 Math Background for ML — CMU
- MIT 18.065 Matrix Methods in Data Analysis, Signal Processing, and Machine Learning
- 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
- Mathematics for Machine Learning, Lectures by Ulrike von Luxburg — Tübingen Machine Learning
Web Programming and Internet Technologies
- 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)
- 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
Theoretical CS and Programming Languages
- MOOC — Compilers — Stanford University
- CS 164 Hack your language, UC Berkeley (Lectures — Youtube)
- Theory of computation — Shai Simonson
- CS 173 Programming Languages, Brown University (Book)
- 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)
- Philip Wadler Haskell lecture recordings
- 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
- 2012 Lectures
- 2013 Lectures
- 2014 Lectures
- 2015 Lectures
- 2016 Lectures
- 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
Embedded Systems
- EE319K Embedded Systems — UT Austin
- EE445L Embedded Systems Design Lab, Fall 2015, UTexas
- CS149 Embedded Systems — Fall 2014 — UCBerkeley
- ECE 4760 Designing with Microcontrollers Fall 2016, Cornell University (Lectures — Youtube)
- ECE 5760 — Advanced Microcontroller Design and system-on-chip, Spring 2016 — Cornell University
- CSE 438/598 Embedded Systems Programming ASU
- Summer Short Course on Embedded Systems Programming
- Internet of Things by Prof. Dr.-Ing. Dietmar P. F. Möller
- 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
- Embedded Systems Class — Version 1–2011 — UNCC
- Embedded Systems using the Renesas RX63N Processor — Version 3 — UNCC
- ELEC2142 — Embedded Systems Design — UNSW
- Software Engineering for Embedded Systems (WS 2011/12) — HPI Univesrity 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 Univesrity of Potsdam
- EE260 Embedded Systems by Robert Paz
- IoT Summer School
- ECSE 421 — Embedded Systems — McGill
- EE402 — Object-oriented Programming with Embedded Systems
- NOC:Advanced IOT Applications — IISc Bangalore
- NOC:Design for internet of things — IISc Bangalore
Computer Organization and Architecture
Computer Organization
- How Computers Work — Aduni
- CS 61C — Machine Structures, UC Berkeley (Lectures — InfoCoBuild)
- 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)
- CS 152 Computer Architecture and Engineering, UC Berkeley
- 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
Parallel Computer Architecture
- 15–418 — Parallel Computer Architecture and Programming, CMU (Lecture Videos)
- CS 267 Applications of Parallel Computers, Spring 16 — UC Berkeley (YouTube)
- MOOC — Heterogeneous Parallel Programming — Coursera
- ECE 498AL — Programming Massively Parallel Processors
- Parallel Computing — IIT Delhi
- Parallel Architectures 2012/13- University of Edinburgh
Digital Systems Design
- ELEC2141 Digital Circuit Design, UNSW
- Digital Systems Design — IIT Kharagpur
- Digital Design Course — 2015 — UNCC
- CS1 — Higher Computing — Richard Buckland UNSW
- MOOC — From NAND to TetrisBuilding 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
- Design of Digital Circuits — ETH Zürich — Spring 2019
- Onur Mutlu @ TU Wien 2019 — Memory Systems
- Memory Systems Course — Technion, Summer 2018
Security
- Internet Security (WT 2018/19) — HPI University of Potsdam
- 6.858 Computer Systems Security — MIT OCW
- CS 161: Computer Security, UC Berkeley
- 6.875 — Cryptography — Spring 2018- MIT
- 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 Analysus — Bill Buchanan — Edinburgh Napier
- Internet Security for Beginners by Dr. Christoph Meinel — HPI
Computer Graphics
- CS184 — Computer Graphics, Fall 2012 — UC Berkeley
- 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
- CSCI E-234 — Introduction to Computer Graphics and GPU Programming, Harvard Extension School
- Computer Graphics Fall 2011, Barbara Hecker
- Introduction to Graphics Architecture
- Ray Tracing for Global Illumination, UCDavis
- Rendering / Ray Tracing Course, SS 2015 — TU Wien
- ECS 178 Introduction to Geometric Modeling, Fall 2012, UC Davis (iTunes)
- Computational Geometry — IIT Delhi
- CS 468 — Differential Geometry for Computer Science — Stanford University (Lecture videos)
- CMU 15–462/662: Computer Graphics
Image Processing and Computer Vision
- MOOC — Digital Image procesing — Duke/Coursera
- Computer Vision 2011 — EPFL, Switzerland
- Digital Image Processing — IIT Kharagpur
- Image Processing and Analysis — UC Davis
- CS 543 — Computer Vision — Spring 2017 (Recordings)
- CAP 5415 — Computer Vision — University of Central Florida(Video Lectures)
- EE225B — Digital Image Processing, Spring 2014 — UC Berkeley (Videos — Spring 2006)
- 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)
- EGGN 510 — Image and Multidimensional Signal Processing — Colorado School of Mines
- 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
- 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
- 3D Coordinate Systems — Remote Course (GE, 2020) — University of Bonn (2013 lectures)
- Modern C++ Course For CV (2020) — University of Bonn
- Photogrammetry 1 Course — 2020 — University of Bonn
- Photogrammetry II Course 2020/21 — University of Bonn
Computational Biology
- 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
- 6.047/6.878 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
Quantum Computing
- 15–859BB: Quantum Computation and Quantum Information 2018 — CMU (Youtube)
- Quantum Mechanics for Scientists and Engineers
- Quantum Mechanics and Quantum Computation — Umesh Vazirani
- 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
Robotics
- CS 223A — Introduction to Robotics, Stanford University
- 6.832 Underactuated Robotics — MIT OCW
- CS287 Advanced Robotics at UC Berkeley Fall 2019 — Instructor: Pieter Abbeel
- CS 287 — Advanced Robotics, Fall 2011, UC Berkeley (Videos)
- 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)
- 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
- Introduction to Vision and Robotics 2015/16- University of Edinburgh
- 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
- 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 — Youtube
- 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)
- Mobile Sensing and Robotics 1 — Part Stachniss (Jointly taught with PhoRS) — University of Bonn
- Mobile Sensing and Robotics 2 — Stachniss & Klingbeil/Holst — University of Bonn
Computational Finance
- COMP510 — Computational Finance — Steven Skiena — 2007 HKUST
- MOOC — Mathematical Methods for Quantitative Finance, University of Washington/Coursera)
- 18.S096 Topics in Mathematics with Applications in Finance, 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
HCI
- CS147 — Introduction to Human-Computer Interaction Design — Stanford
- CSEP 510 — Human Computer Interaction
- Programming for Designers — COMP1400-T2 (2010) — UNSW
- 08–763 Intro to HCI for Technology Executives — Fall 2015 — CMU
- 05–600 HCI Pro Seminar — Fall 2015 — CMU
- Game Development
- MIT CMS.611J Creating Video Games, Fall 2014
- MOOC — Beginning Game Programming with C# — Coursera
Geospatial
- Introduction to Spatial Data Science, Autumn 2016, University of Chicago
- Spatial Regression Analysis, Spring 2017, University of Chicago
- Spatial Data Science, Autumn 2017, University of Chicago
- Introduction to Geographic Information Systems — IIT Roorkee
- SCICOMP — An Introduction to Efficient Scientific Computation, Universität Bremen
- CS E-259 XML with Java, Java Servlet, and JSP — Harvard
- CSE 40373 — Spr 2009: Multimedia Systems
- Exposing Digital Photography — Harvard Extension School
- MOOC — Matlab — Coursera
- Computing for Computer Scientists — University of Michigan
- Linux Implementation/Administration Practicum — Redhat by Tulio Llosa
- SIMS 141 — Search Engines — Fall 2005 UCBerkeley
- Innovative Computing — Harvard University
- Linux Programming & Scripting — IIT Madras
- Model Checking — IIT Madras
- Virtual Reality — IIT Madras
- CS 195 — Social Implications of Computing, Spring 2015 — UC Berkeley (YouTube)
- Spatial Databases and Geographic Information Systems — Technische Universität Braunschweig, Germany (in German)
- 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
Blockchain and Cryptocurrencies
- 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
Thanks to : Developer — Y
