📚 Textbooks

There is not a single textbook. Suggested are as follows; The first two are the ones we used. For Deep Learning part we use “Dive into Deep Learning”.

Topic	Authors	Book
ML/Statistical Inference	Friedman, Tibshirani, Hastie	“The Elements of Statistical Learning”
Generic ML	Christopher M. Bishop	“Pattern Recognition and Machine Learning”
Generic ML	H. Daumé III	“A Course in Machine Learning”
Generic ML	Kevin P. Murphy	“Probabilistic Machine Learning: An introduction”, MIT Press, 2021
Math for ML	Deisenroth, Faisal, Ong	“Mathematics for Machine Learning”, Cambridge University Press, 2021
Deep Learning	Ian Goodfellow and Yoshua Bengio and Aaron Courville	“Deep Learning”, MIT Press 2016
Deep Learning	Ston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola	“Dive into Deep Learning”
Deep Learning	Simone Scardapane (from Sapienza!)	“Alice’s Adventures in a differentiable wonderland”

You can find online most of these or part of them.

• The course is inspired and follows CS229 by Stanford