/SelfStudy

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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