Learning AI

Advices from Open AI, Facebook AI leaders

Courses You MUST Take:

1. Machine Learning by Andrew Ng (https://www.coursera.org/learn/machine-learning) /// Class notes: (http://holehouse.org/mlclass/index.html)
2. Yaser Abu-Mostafa’s Machine Learning course which focuses much more on theory than the Coursera class but it is still relevant for beginners.(https://work.caltech.edu/telecourse.html)
3. Neural Networks and Deep Learning (Recommended by Google Brain Team) (http://neuralnetworksanddeeplearning.com/)
4. Probabilistic Graphical Models (https://www.coursera.org/learn/probabilistic-graphical-models)
5. Computational Neuroscience (https://www.coursera.org/learn/computational-neuroscience)
6. Statistical Machine Learning (http://www.stat.cmu.edu/~larry/=sml/)

From Open AI CEO Greg Brockman on Quora

1. Deep Learning Book (http://www.deeplearningbook.org/) ( Also Recommended by Google Brain Team )

It contains essentially all the concepts and intuition needed for deep learning engineering (except reinforcement learning). by Greg

2. If you’d like to take courses:

• Linear Algebra — Stephen Boyd’s EE263 (Stanford) (http://ee263.stanford.edu/) or Linear Algebra (MIT)(http://ocw.mit.edu/courses/mathematics/18-06sc-linear-algebra-fall-2011/index.htm)
• Neural Networks for Machine Learning — Geoff Hinton (Coursera) https://www.coursera.org/learn/neural-networks
• Neural Nets — Andrej Karpathy’s CS231N (Stanford) http://cs231n.stanford.edu/
• Advanced Robotics (the MDP / optimal control lectures) — Pieter Abbeel’s CS287 (Berkeley) https://people.eecs.berkeley.edu/~pabbeel/cs287-fa11/
• Deep RL — John Schulman’s CS294–112 (Berkeley) http://rll.berkeley.edu/deeprlcourse/

From Director of AI Research at Facebook and Professor at NYU Yann LeCun on Quora

In any case, take Calc I, Calc II, Calc III, Linear Algebra, Probability and Statistics, and as many physics courses as you can. But make sure you learn to program.