huiwenluo/selfstudy

Here you'll find materials I've used for self-studying over the years. Eventually, after many years, I'll get through all of them.

I claim no authorship for any of the documents collected in this repository! Expect to see a lot of copy-paste of relevant snippets.

Probability and Statistics

• Cosma Shalizi's forthcoming Advanced Data Analysis from an Elementary Point of View

• Gelman et al's forthcoming Regression and Other Stories

• Bob Carpenter's forthcoming book on Probability

• Aronow & Miller's Foundations of Agnostic Statistics

• Charles Manski's Identification for Prediction and Decision

• Wilke et al's Spatio-Temporal Statistics with R

• Gelman et al's Bayesian Data Analysis 3

• Rasmussen & William's Gaussian Processes for Machine Learning

Machine Learning and Computer Science

• Boyd & Vandenberghe's Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares

• Jurafsky & Martin's Speech and Language Processing

• Gilbert Strang's Linear Algebra and Learning from Data

• Francois Chollet's Deep Learning with Python

• Eugene Charniak's Introduction to Deep Learning

• Aurélien Géron Hands-on Machine Learning with Scikit-Learn, Keras, and TensorFlow

• Kleinberg & Tardos' Algorithm Design

• Lewis & Zax's Essential Discrete Mathematics for Computer Science

Social Networks

• Rafael Pass' A Course in Networks and Markets

• Mark Newman's Networks

• Easley & Kleinberg's Networks, Crowds, and Markets