Stanford

  • Andrew Ng. CS229: Machine Learning. Stanford.
  • Andrew Ng et al. UFLDL: Deep Learning Tutorial. Stanford. [Proposal]
  • Silvio Savarese. CS231A: Computer Vision: from 3D reconstruction to recognition. Stanford.
  • Fei-Fei Li, Andrej Karpathy, and Justin Johnson. CS231N: Convolutional Neural Networks for Visual Recognition. Stanford.
  • Daphne Koller. CS228: Probabilistic Graphical Models. Stanford.
  • Chris Manning & Richard Socher. CS224N: Natural Language Processing with Deep Learning. Stanford.

MIT

  • David Jerison. 18.01: Single Variable Calculus. MIT.
  • Denis Auroux. 18.02: Multivariable Calculus. MIT.
  • Gilbert Strang. 18.06: Linear Algebra. MIT. 2011
  • John Guttag. 6.00: Introduction to Computer Science and Programming. MIT.
  • Dennis Freeman. 6.01: Introduction to Electrical Engineering and Computer Science I. MIT.
  • John Tsitsiklis & Patrick Jaillet. 6.041: Introduction to Probability - The Science of Uncertainty. MIT.
  • Albert Meyer and Adam Chlipala. 6.042: Mathematics for Computer Science. MIT.
  • Erik Demaine and Srinivas Devadas. 6.006: Introduction to Algorithms. MIT.
  • Sarina Canelake. 6.189: A Gentle Introduction to Programming Using Python. MIT.

Coursera

  • Andrew Ng. Machine Learning. Stanford. [Grade: 100%]
  • Hsuan-Tien Lin. Machine Learning Techniques. National Taiwan University. [Grade: 100%]
  • Michael Fitzpatrick. Introduction to Programming with MATLAB. Vanderbilt University.
  • Charles Severance. Python Data Structures. University of Michigan.
  • Charles Severance. Using Database with Python. University of Michigan.
  • Speaking English Professionally: In Person, Online & On the phone. GIT

edX

  • Bill Aulte and Erdin Beshimov. Entrepreneurship 101: Who are your customer? MIT
  • Bill Aulte and Erdin Beshimov. Entrepreneurship 102: What can you do for your customer? MIT
  • Alan V. Oppenheim and Tom Baran. Discrete-Time Signal Processing. MIT