Table of Contents
- Introduction to Computer Science
- Data Structures and Algorithms
- Systems Programming
- Database Systems
- Software Engineering
- Artificial Intelligence
- Machine Learning
- Web Programming and Internet Technologies
- Computer Networks
- Math for Computer Science
- 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
- Computational Finance
- Blockchain Development
- Miscellaneous Topics
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
- 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
- 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
- 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(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
- Data Structures – mycodeschool
Systems Programming
- 15-213 Introduction to Computer Systems, Fall 2015 – CMU
- 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
- ECS 150 – Operating Systems and Systems Programming – Fall 2020 – Joël Porquet-Lupine – UC Davis
- 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 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)
- 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 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-2017, YouTube-2018, YouTube-2019, YouTube-2021, YouTube-2022)
- 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
- 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
- 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
- Software Testing – IIT Kharagpur
- Software Testing – Udacity, course-cs258 | 2015
- Software Debugging – Udacity, course-cs259 | 2015
- Software Engineering – Bauhaus-Uni Weimar
- 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
- 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
- T81-558: Applications of Deep Neural Networks by Jeff Heaton, 2022, Washington University in St. Louis
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
- 6.036 – Machine Learning, Broderick – MIT Fall 2020
- Applied Machine Learning (Cornell Tech CS 5787, Fall 2020)
- Stanford CS229: Machine Learning Course | Summer 2019 (Anand Avati)
- 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
- 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
- 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
- Python Data Science with the TCLab (YouTube)
- 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
- 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
- Intro to Deep Learning and Generative Models Course – Prof Sebastian Raschka
- Deep Learning CMU
- CS231n Deep Learning for Computer Vision – Winter 2016 Andrej Karpathy – Stanford University
- Deep Learning: CS 182 Spring 2021
- 10-414/714: Deep Learning Systems – CMU (Youtube)
- Part 1: Practical Deep Learning for Coders, v3 – fast.ai
- Part 2: Deep Learning from the Foundations – fast.ai
- Deep learning at Oxford 2015 – Nando de Freitas
- Self-Driving Cars — Andreas Geiger, 2021/22 (YouTube)
- 6.S094: Deep Learning for Self-Driving Cars – MIT
- CS294-129 Designing, Visualizing and Understanding Deep Neural Networks (YouTube)
- CS230: Deep Learning – Autumn 2018 – Stanford University
- 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
- Geometric Deep Learning – AMMI
- Math for Deep Learning — Andreas Geiger
- Applied Deep Learning 2022 – TU Wien
- Neural Networks: Zero to Hero – Andrej Karpathy
- CIS 522 – Deep Learning – U Penn
- UVA DEEP LEARNING COURSE
- Deep Learning (Fall 2020) – Georgia Tech
- 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
- 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
- Advanced Machine Learning
- Natural Language Processing
- CS 224N – 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
- 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
- Accelerated Natural Language Processing 2015/16- University of Edinburgh
- Natural Language Processing – IIT Bombay
- CMU Advanced NLP 2021
- CMU Neural Nets for NLP 2021
- Natural Language Processing – Michael Collins – Columbia University
- CMU CS11-737 – Multilingual Natural Language Processing
- UMass CS685: Advanced Natural Language Processing (Spring 2022)
- Natural Language Processing (CMSC 470)
- ML based Computer Vision
- CS 231n – Convolutional Neural Networks for Visual Recognition, Stanford 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
- NOC:Deep Learning For Visual Computing – IIT Kharagpur
- Deep Learning for Computer Vision – University of Michigan
- Time Series Analysis
- Misc Machine Learning Topics
- EE364a: Convex Optimization I – Stanford University
- 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)
- 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
- CS224W Machine Learning with Graphs | Spring 2021 | Stanford University
- 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
- Introduction to Data-Centric AI – MIT
- Parallel Computing and Scientific Machine Learning
Web Programming and Internet Technologies
- CS50’s Web Programming with Python and JavaScript
- Web Design Decal – HTML/CSS/JavaScript Course, University of California, Berkeley
- CS 75 Building Dynamic Websites – Harvard University
- Internet Technology – IIT Kharagpur
- Introduction to Modern Application Development – IIT Madras
- CSE 199 – How the Internet Works, Fall 2016 – University of Buffalo
- Open Sourced Elective: Database and Rails – Intro to Ruby on Rails, University of Texas (Lectures – Youtube)
- 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
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 Science
- Maths courses all topics covered
- Calculus
- Discrete Math
- 6.042J – Mathematics for Computer Science, MIT OCW
- 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
- Statistics – CrashCourse
- 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
- Mathematical Foundations of Machine Learning (Fall 2021) – University of Chicago – Rebecca Willett
- 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)
- ENGR108: Introduction to Applied Linear Algebra—Vectors, Matrices, and Least Squares – Stanford University
- 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
- Numerics of Machine Learning (Winter 2022/23) – Tübingen Machine Learning
Theoretical CS and Programming Languages
- MOOC – Compilers – Stanford University
- CS 6120: Advanced Compilers: The Self-Guided Online Course – Cornell University
- CS 164 Hack your language, UC Berkeley (Lectures – Youtube)
- Theory of computation – Shai Simonson
- CS 173 Programming Languages, Brown University (Book)
- CS 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 (2021) – University of Nottingham
- Functional Programming – University of Edinburgh – 2016-17
- MOOC – Functional Programming Principles in Scala by Martin Odersky
- CS294 – Program Synthesis for Everyone
- MOOC – Principles of Reactive Programming, Scala – Coursera
- Category Theory for Programmers, 2014 – Bartosz Milewski (YouTube)
- Oregon Programming Languages Summer School (Proof theory, type theory, category theory, verification)
- Inf1 – Computation and Logic 2015 – University of Edinburgh
- INFORMATICS 1 – FUNCTIONAL PROGRAMMING – University of Edinburgh (Videos)
- Compiler Design – IISC Bangalore
- Compiler Design – IIT Kanpur
- Principles of Programming Languages – IIT Delhi
- Principles of Compiler Design – IISC Bangalore
- Functional Programming in Haskell – IIT Madras
- Theory of Computation – IIT Kanpur
- Theory of Automata, Formal Languages and Computation – IIT Madras
- Theory of Computation – IIT Kanpur
- Logic for CS – IIT Delhi
- Principles of Compiler Design – Swarthmore College
- Undergrad Complexity Theory at CMU
- Graduate Complexity Theory at CMU
- Great Ideas in Theoretical Computer Science at CMU
- Analysis of Boolean Functions at CMU
- Theoretical Computer Science (Bridging Course)(Tutorial) – SS 2015
- Languages & Translators – UCLouvain LINFO2132
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 University of Potsdam
- Embedded Software Testing – IIT Madras
- Embedded Systems – IIT Delhi
- Embedded Systems Design – IIT Kharagpur
- ARM Based Development – IIT Madras
- Software Engineering for Self Adaptive Systems – iTunes – HPI University of Potsdam
- EE260 Embedded Systems by Robert Paz
- IoT Summer School
- ECSE 421 – Embedded Systems – McGill
- EE402 – Object-oriented Programming with Embedded Systems
- NOC:Advanced IOT Applications – IISc Bangalore
- NOC:Design for internet of things – IISc Bangalore
Real-Time System Evaluation
- Performance evaluation of Computer systems – IIT Madras
- Real Time systems – IIT Karaghpur
- EE 380 Colloquim on Computer Systems – Stanford University
- System storages – 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
- BE5B35APO – Computer Architectures, Spring 2022, CTU – FEE (YouTube – Spring 2022) (RISC-V simulator – QtRvSim)
- 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
- CS1 – Higher Computing – Richard Buckland UNSW
- MOOC – From NAND to Tetris – Building a Modern Computer From First Principles (YouTube)
- System Validation, TU Delft
- High Performance Computing – IISC Bangalore
- Introduction to ARM – Open SecurityTraining
- Intro x86 (32 bit) – Open SecurityTraining
- Intermediate x86 (32 bit) – Open SecurityTraining
- 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 253 Web Security – Stanford University
- 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 Analysis – Bill Buchanan – Edinburgh Napier
- Internet Security for Beginners by Dr. Christoph Meinel – HPI
- Offensive Security and Reverse Engineering, Chaplain University by Ali Hadi
Books
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 processing – 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
- 3D Computer Vision – National University of Singapore
Computational Physics
- Astronomical data analysis using Python 2021 – NRC IUCAA
- SPARC Workshop on Machine Learning in Solar Physics and Space Weather – CESSI IISER Kolkata
- Computational Astrophysics – AstroTwinCoLo, 2015
- Astroinformatics 2019 Conference – Caltech
- Space Science with Python – Astroniz
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.S897 Machine Learning For Healthcare
- 6.047/6.878 Machine Learning for Genomics Fall 2020 – MIT
- 6.874 MIT Deep Learning in Life Sciences – Spring 2021 – MIT
- 6.047/6.878 Public Lectures on Computational Biology: Genomes, Networks, Evolution – MIT
- Bio 84 – Your Genes and Your Health, Stanford University
- BioMedical Informatics 231 Computational Molecular Biology, Stanford University
- BioMedical Informatics 258 Genomics, Bioinformatics & Medicine, Stanford University
- 03-251: Introduction to Computational Molecular Biology – Carnegie Mellon University
- 03-712: Biological Modeling and Simulation – Carnegie Mellon University
- MOOC – Bioinformatics Algorithms: An Active Learning Approach – UC San Diego/Coursera
- Neural Networks and Biological Modeling – Lecturer: Prof. Wulfram Gerstner – EPFL
- Video Lectures of Wulfram Gerstner: Computational Neuroscience – EPFL
- An Introduction To Systems Biology
- Introduction to Bioinformatics, METUOpenCourseWare
- MOOC – Algorithms for DNA Sequencing, Coursera
- Frontiers of Biomedical Engineering with W. Mark Saltzman – Yale
- NOC:Computational Systems Biology – IIT Madras
- NOC:BioInformatics:Algorithms and Applications – IIT Madras
- Data Science and AI for Neuroscience Summer School – Caltech Neuroscience
Quantum Computing
- 15-859BB: Quantum Computation and Quantum Information 2018 – CMU (Youtube)
- Ph/CS 219A Quantum Computation – Prof Preskill – Caltech
- Quantum Mechanics and Quantum Computation – Umesh Vazirani
- Phys 1470 – Foundations of Quantum Computing and Quantum Information – U of Pittsburgh
- Quantum Physics for Non-Physicists 2021 – ETH Zurich (2020)
- Quantum Information and Computing by Prof. D.K. Ghosh
- Quantum Computing by Prof. Debabrata Goswami
- The Building Blocks of a Quantum Computer: Part 1 – TU Delft
- The Building Blocks of a Quantum Computer: Part 2 – TU Delft
- Quantum Cryptography – TU Delft
- Introduction to Quantum Information
- Quantum Computing for Everyone — Part 1 (Part 2)
- Quantum Computer Systems – UChicago
- Quantum computing for the determined – Michael Nielsen
- Quantum Computing
Books
Robotics
- ROB 101: Computational Linear Algebra – University of Michigan (Youtube – Fall 2021)
- ROB 102: Introduction to AI and Programming – University of Michigan
- ROB 320: Robot Operating Systems – University of Michigan
- ROB 501: Mathematics for Robotics – University of Michigan (Youtube)
- ROB 530 MOBILE ROBOTICS at U of Michigan – WINTER 2022 — Instructor: Maani Ghaffari
- Autorob Winter 2022 – University of Michigan
- DeepRob Winter 2023 – University of Michigan
- CS 223A – Introduction to Robotics, Stanford University
- 6.832 Underactuated Robotics – MIT OCW
- 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
- Aerial Robotics – University of Pennsylvania (UPenn)
- Modern Robotics – Northwestern University
- MIT 6.4210/6.4212 – Robotic Manipulation – MIT (Youtube)
- Industrial Robotics and Automation – IIT (ISM) Dhanbad
- MEE5114 Advanced Control for Robotics from Southern University of Science and Technology
- Self-Driving Cars — Andreas Geiger
Books
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
Blockchain Development
Whitepaper and other Important books
- Blockchain and Cryptocurrencies
- Blockchain, Solidity, and Full Stack Web3 Development with JavaScript
- Blockchain Fundamentals Decal 2018 – Berkeley DeCal
- Blockchain for Developers Decal – Spring 2018 – Berkeley DeCal
- Cryptocurrency Engineering and Design – Spring 2018 – MIT
- 15.S12 Blockchain and Money, Fall 2018 – MIT
- Blockchain – Foundations and Use Cases
- Become Blockchain Developer
Miscellaneous Topics
- HCI
- Game Development
- Geospatial
- 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
- The Missing Semester of Your CS Education
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