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Computer Science video courses
Introduction
List of Computer Science courses with video lectures. For more courses, please refer MIT OCW and awesome-courses
Focus would be to act as general undergraduate/graduate CS University curriculum. Brevity would be preferred over comprehensiveness/details in order to maintain the list readable and usable. Please visit link to course for more details.
Table of Contents
- Introduction to CS
- Data Structures and Algorithms
- Systems
- Artificial Intelligence
- Machine Learning
- Computer Graphics
- CS Theory
- Programming Languages / Compilers
- Security
- Misc
Courses
Introduction to CS
- 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
- 6.001 - Structure and Interpretation of Computer Programs, MIT (Textbook)
- CS 50 - Introduction to Computer Science, Harvard University
- CS 61A - Structure and Interpretation of Computer Programs [Python], UC Berkeley
- CS 101 - Computer Science 101, Stanford University (Register free to access Videos)
- CS 106A - Programming Methodology, Stanford University
- CS 106B - Programming Abstractions, Stanford University
- CS 107 - Programming Paradigms, Stanford University
Data Structures and Algorithms
- CS 61B - Data Structures, UC Berkeley
- 6.006 - Introduction to Algorithms, MIT OCW
- CSE 373 - Analysis of Algorithms, Stony Brook - Prof Skiena
- 6.046J - Introduction to Algorithms - Fall 2005, MIT OCW
- 6.046 - Design and Analysis of Algorithms, Spring 2015 - MIT OCW
- Programming Challenges - Prof Skiena
- 16s-4102 - Algorithms, University of Virginia (Youtube)
- CS 261 - A Second Course in Algorithms, Stanford University (Lectures) (Youtube)
- CS 224 - Advanced Algorithms, Harvard University (Lecture Videos) (Youtube)
- ECS 122A - Algorithm Design and Analysis, UC Davis
- CS 6150 - Advanced Algorithms (Fall 2016), 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)
Systems
- 6.033 Computer System Engineering - MIT
- CS 162 - Operating Systems and Systems Programming, UC Berkeley (Lectures - YouTube)
- CS 4414 - Operating Systems, University of Virginia
- CSE 421/521 - Introduction to Operating Systems, SUNY University at Buffalo, NY - Spring 2016 (Lectures - YouTube)
- CS 377 Fall 16: Operating Systems - Umass OS
- 6.828: Operating System Engineering [Fall 2014]
- VU:Distributed Systems: Principles and Paradigms by Maarten van Steen (Fall 2012), Vrije Universiteit, Amsterdam
- CS 5530 - Database Systems, Spring 2016, University of Utah
- 15-721 - Database Systems, CMU (Lectures - YouTube)
- CS 186 - Database Systems, UC Berkeley, Spring 2015 (Lectures- YouTube)
- CS 6530 - Graduate-level Database Systems, Fall 2016, University of Utah (Lectures - YouTube)
- CS 677 Spring 16: Distributed Operating Systems - Umass OS
- CS 436: Distributed Computer Systems - U Waterloo
- 6.824: Distributed Systems, Spring 2015 - MIT
- CS194 Advanced Operating Systems Structures and Implementation, Spring 2013, UC Berkeley
- 6.858 Computer Systems Security - MIT OCW
Machine Learning
- StatLearning - Intro to Statistical Learning, Stanford University
- CS 156 - Learning from Data, Caltech
- 10-601 - Machine Learning, Carnegie Mellon University
- Microsoft Research - Machine Learning Course
- CS 446 - Machine Learning, Fall 2016, UIUC(Fall 2015 Lectures)
- undergraduate machine learning at UBC 2012, Nando de Freitas
- CS 229 - Machine Learning - Stanford University
- CS 5140/6140 - Data Mining, Spring 2016, University of Utah (Lectures - Youtube)
- CS 5350/6350 - Machine Learning, Fall 2016, University of Utah
- CS 6190 - Probabilistic Modeling, Spring 2016, University of Utah
- CS 6955 - Clustering, Spring 2015, University of Utah
- DS-GA 1008 - Deep Learning, New York University
- Info 290 - Analyzing Big Data with Twitter, UC Berkeley school of information
- Machine Learning 2013 - Nando de Freitas, UBC
- Machine Learning: 2014-2015, University of Oxford
- Deep learning at Oxford 2015 - Nando de Freitas
- 10-701 Machine Learning - Tom Mitchell, Spring 2011, Carnegie Mellon University (Fall 2014)
- 10-702/36-702 - Statistical Machine Learning - Larry Wasserman, Spring 2016, CMU
- 10-708 - Probabilistic Graphical Models, Carnegie Mellon University
- 36-705 - Intermediate Statistics - Larry Wasserman, CMU
- CS 224d - Deep Learning for Natural Language Processing, Stanford University (Lectures - Youtube)
- CS 229r - Algorithms for Big Data, Harvard University (Youtube)
- CS 231n - Convolutional Neural Networks for Visual Recognition, Stanford University
- CAM 383M - Statistical and Discrete Methods for Scientific Computing, University of Texas
###Artificial Intelligence
- CS 188 - Introduction to Artificial Intelligence, UC Berkeley
- 6.034 Artificial Intelligence, MIT OCW
Computer Graphics
- CAP 5415 - Computer Vision, University of Central Florida
- CS 5630/6630 - Visualization, Fall 2016, University of Utah (Lectures - Youtube)
Misc
- CS 193a - Android App Development, Spring 2016, Stanford University
- Skiena's Computational Biology Lectures
- Skiena's Computational Finance Lectures
- AM 207 - Monte Carlo Methods and Stochastic Optimization, Harvard University
- CS 193p - Developing Applications for iOS, Stanford University
- CS 223A - Introduction to Robotics, Stanford University
- CS 411 - Software Architecture Design, Bilkent University
- CS 3152 - Introduction to Computer Game Development, Cornell University
- Open Sourced Elective: Database and Rails - Intro to Ruby on Rails, University of Texas (Lectures - Youtube)
- SCICOMP - An Introduction to Efficient Scientific Computation, Universität Bremen (Lectures - Youtube)