Information Theory
- Information Theory, Inference, and Learning Algorithms by David MacKay.
ML
An Introduction to Statistical Learning by Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani.
Python Data Science Handbook by Jake VanderPlas.
Dive into Deep Learning by Aston Zhang, Zachary C. Lipton, Mu Li, and Alexander J. Smola.
Approaching (Almost) Any Machine Learning Problem by Abhishek Thakur
Bayesian Methods
Think Bayes by Allen B. Downey
Deep Learning
- Deep Learning by Aaron Courville, Ian Goodfellow, and Yoshua Bengio
Reinforcement Learning
Reinforcement Learning: An Introduction by Richard S. Sutton and Andrew G. Barto.
Bandit Algorithms By Tor Lattimore and Csaba Szepesvari
Algorithms for Reinforcement Learning by Csaba Szepesvari
RL and Optimal Control by Dimitri P. Bertsekas
NLP
Speech and Language Processing by Daniel Jurafsky and James H. Martin.
Data Analysis a Bayesian Tutorial Second Edition is recommended by David MacKay.
Teaching Statistics: A Bag of Tricks Andrew Gelman (Author), Deborah Nolan (Author)
Citation
@online{bochman2025,
author = {Bochman, Oren},
title = {Books, {Courses} {Tools}},
date = {2025-01-01},
url = {https://orenbochman.github.io/posts/2025/2025-01-01-books/},
langid = {en}
}