Learn it yourself - online resources

by Justas Dauparas

This is a short list of books and lectures with links for myself and anyone else who might find them useful. A book is worth a thousand arXiv papers!

Machine Learning Books

  1. Pattern Recognition and Machine Learning by Christopher M. Bishop,
  2. Information Theory, Inference, and Learning Algorithms by David J.C. MacKay, videos,
  3. Machine Learning: A Probabilistic Perspective by Kevin P. Murphy,
  4. Bayesian Reasoning and Machine Learning by David Barber,
  5. Deep Learning by Ian Goodfellow, Yoshua Bengio, and Aaron Courville,
  6. The Elements of Statistical Learning by Trevor Hastie, Robert Tibshirani, and Jerome Friedman,
  7. All of Statistics A Concise Course in Statistical Inference by Larry Wasserman,
  8. Reinforcement learning: An introduction by Richard S. Sutton and Andrew G. Barto.

Lectures on Machine Learning

  1. fast.ai,
  2. Natural Language Processing with Deep Learning, CS224n at Stanford,
  3. Convolutional Neural Networks for Visual Recognition CS231n at Stanford,
  4. Deep Unsupervised Learning, CS294-158 at UC Berkeley,
  5. Deep Reinforcement Learning CS285 at UC Berkeley.

Lectures on Theoretical Physics by David Tong

  1. Dynamics and Relativity,
  2. Classical Dynamics,
  3. Electromagnetism,
  4. Topics in Quantum Mechanics,
  5. Solid State Physics,
  6. Quantum Hall Effect,
  7. Statistical Physics,
  8. Kinetic Theory,
  9. Statistical Field Theory,
  10. Quantum Field Theory,
  11. Gauge Theory,
  12. Solitons,
  13. General Relativity,
  14. Cosmology,
  15. String Theory.
Justas Dauparas
Postdoctoral Fellow