Textbooks and other resources

Fraida Fund

Machine Learning Concepts and Theory

The Elements of Statistical Learning, 2nd edition (Hastie, Friedman, and Tibshirani), 2019. PDF
An Introduction to Statistical Learning with Applications in Python, (James, Witten, Hastie, Tibshirani, and Taylor), 2023. PDF
Pattern Recognition and Machine Learning, (Bishop), 2006. PDF
Machine Learning: A Probabilistic Perspective, (Murphy). PDF
Understanding Machine Learning: From Theory to Algorithms, (Shalev-Shwartz, Ben-David), 2014. Website, Download

Python Data Science Handbook, (VanderPlas), 2016. Github
Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow (Géron), 2019. NYU Library 1, NYU Library 2, Notebooks on Github
Machine Learning with PyTorch and Scikit-Learn (Raschka, Liu, Mirjalili), 2022. NYU Library, Notebooks on Github
Python Machine Learning, 3rd edition (Raschka, Mirjalili), 2019. NYU Library, Notebooks on Github

Introduction to Applied Linear Algebra, (Boyd, Vandenberghe). PDF
Summary notes on Linear Algebra (Stanford). PDF
Convex Opimization, (Boyd, Vandenberghe). PDF
Summary notes on Convex Optimization (NYU CDS). PDF
Probability and Statistics for Data Science notes (Carlos Fernandez-Granda, NYU CDS). PDF
Summary notes on Probability (Stanford). PDF

AI for Good Seminar Series. YouTube Playlist
NYU CDS DS-GA 1008: Deep Learning. Course Site
Textbook: Deep Learning, (Goodfellow, Bengio, Courville). Website
Stanford CS231N: Convolutional Neural Networks for Visual Recognition. Course Notes, YouTube
Stanford CS224N: Natural Language Processing with Deep Learning. Course Site, YouTube Playlist
Harvard CS229br: Advanced Topics in Theory of Machine Learning (Boaz Barak). Course Site
UCL (David Silver) Reinforcement Learning. Course Site, YouTube
Textbook: Reinforcement Learning (Sutton, Barto). Website, PDF