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
  • Lecture Resources by Type
  • Lecture Resources by Topic
• 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

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Data Structures and Algorithms

• CS 61B - Data Structures, UC Berkeley
  • Fall 2006 - Prof. Jonathan Shewchuk
  • Spring 16 - Josh Hug
• 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)

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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

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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

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###Artificial Intelligence

• CS 188 - Introduction to Artificial Intelligence, UC Berkeley
• 6.034 Artificial Intelligence, MIT OCW

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Computer Graphics

• CAP 5415 - Computer Vision, University of Central Florida
• CS 5630/6630 - Visualization, Fall 2016, University of Utah (Lectures - Youtube)

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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)

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