Computer Vision: Models, Learning, and Inference
Simon J. D. Prince
Computers and Intractability: A Guide to the Theory of NP-completeness
Michael R. Garey & David S. Johnson
深入理解计算机系统
Randal E. Bryant
Non-Convex Optimization for Machine Learning
Prateek Jain & Purushottam Kar
Machine Learning, Second Edition: A Probabilistic Perspective
Kevin P. Murphy
Information Geometry and Its Applications
Shun-Ichi Amari
Bayesian Data Analysis
Andrew Gelman
Probabilistic Graphical Models: Principles and Techniques
Daphne Koller & Nir Friedman
Information Theory, Inference and Learning Algorithms
David J. C. MacKay
Forecasting Economic Time Series
C. W. J. Granger & Paul Newbold & Karl Shell
Collection of Problems in Probability Theory
L. D. Meshalkin
Statistical Rethinking: A Bayesian Course With Examples in R ...
Richard McElreath
Pen and Paper Exercises in Machine Learning
Michael U. Gutmann