计算理论导引
Michael Sipser
计算机网络: 自顶向下方法
James F. Kurose & Keith W. Ross
Recommender Systems: The Textbook
Charu C. Aggarwal
The Design of Approximation Algorithms
David P. Williamson & David B. Shmoys
Statistical Rethinking: A Bayesian Course With Examples in R ...
Richard McElreath
Probabilistic Graphical Models: Principles and Techniques
Daphne Koller & Nir Friedman
Physics-based Deep Learning
N. Thuerey, B. Holzschuh, P. Holl, G. Kohl, M. Lino, Q. Liu, ...
Pen and Paper Exercises in Machine Learning
Michael U. Gutmann
Pattern Recognition and Machine Learning: Solutions to Exercises ...
Markus Svensén & Christopher M. Bishop
Pattern Recognition and Machine Learning
Christopher M. Bishop
Non-Convex Optimization for Machine Learning
Prateek Jain & Purushottam Kar
Multi-Agent Reinforcement Learning: Foundations and Modern Approaches
Stefano V. Albrecht & Filippos Christianos & Lukas Schäfer
Matrix Calculus (for Machine Learning and Beyond)
Alan Edelman, Steven G. Johnson
Machine Learning, Second Edition: A Probabilistic Perspective
Kevin P. Murphy
Hyperparameter Optimization in Machine Learning
Luca Franceschi
Computer Vision: Models, Learning, and Inference
Simon J. D. Prince