Openware Course List

Openware Course List

This is an archive of openware courses related to computer science, machine learning, physics and mathematics.

Computer Science Fundamentals

  1. Berkley CS61A: The Structure and Interpretation of Computer Programs
  2. Berkley CS61B: Data Structure
  3. Berkley CS61C: Great Ideas in Computer Architecture
  4. MIT 6.006 Introduction to Algorithms
  5. MIT 6.046J Design and Analysis of Algorithms

Network

  1. Harvard CS E-75 Building Dynamic Websites

Artifitial Intelligence

  1. Berkley CS188x: Artificial Intelligence
  2. MIT 6.034: Artificial Intelligence

Machine Learning

  1. Stanford CS229: Machine Learning
  2. CMU 10-601: Machine Learning by Tom Mitchell & Maria-Florina Balcan
  3. CMU 10-601: Machine Learning by Roni Rosenfeld
  4. CMU 10-701: Machine Learning by Tom Mitchell (2011)
  5. CMU 10-701: Machine Learning by Eric Xing & Matt Gormley
  6. CMU 10-701: Machine Learning by Alex Smola
  7. CMU 10-715: Advanced Introduction to Machine Learning
  8. CMU 10-702 Statistical Machine Learning
  9. CMU 10-605 Machine Learning with Large Datasets
  10. UBC CPSC 540 Machine Learning
  11. Caltech Learning from Data
  12. Oxford Machine Learning
  13. Berkley CS 189 Introduction to Machine Learning (Video Removed)
  14. Stanford Statistical Learning
  15. Berkley CS 281B Scalable Machine Learning

Graphical Model

  1. CMU 10-708 Probabilistic Graphical Model by Eric Xing
  2. EE512A Advanced Inference in Graphical Models

Deep Learning

  1. Stanford CS231n: Convolutional Neural Networks for Visual Recognition
  2. Berkley CS294: Deep Reinforcement Learning
  3. MIT 6.S094: Deep Learning for Self-Driving Cars
  4. Stanford CS224n: Natural Language Processing with Deep Learning

Optimization

  1. Stanford EE364a Convex Optimization I
  2. Stanford EE364b Convex Optimization II
  3. CMU 10-725 Convex Optimization
  4. CMU 10-801 Advanced Optimization and Randomized Methods

Mathematics

  1. MIT 18.06 Linear Algebra
  2. MIT 6.041 Probabilistic Systems Analysis and Applied Probability
  3. Cambridge Information Theory, Pattern Recognition, and Neural Networks

Physics

  1. Theoretical Minimum Series by Leonard Susskind
  2. MIT 8.04 Quantum Physics
  3. International Winter School on Gravity and Light
  4. PSI Fellow Lie Groups and Lie Algebras

Finance

  1. MIT 18.S096 Topics in Mathematics w Applications in Finance