ai_projects

AI related projects -- learning progress

• Books and papers to take a look at:

  • Geometric deep learning (Deep mind + Cambridge) and accompanying course (Bronstein et al.)
    • Related review and accompanying code
  • Deep Learning book - I. Goodfellow et al.
  • Machine Learning: A probabilistic perspective - K. Murphy
  • Mathematics for Machine Learning - C. S. Ong, A. A. Faisal, M. P. Deisenroth
  • Christopher, M. B. (2016). Pattern Recognition and Machine Learning. Springer-Verlag New York.
  • Friedman, J., Hastie, T., & Tibshirani, R. (2001). The elements of statistical learning (Vol. 1, pp. 337-387). New York: Springer series in statistics.
  • Statistical physics of data assimilation and machine learning
  • Understanding Deep Learning - J.D.Prince

• Additional paper reading material:

  • ML @ Berkeley
  • ML @ Berkeley reading list