Reading, Writing, and Proving: A Closer Look at Mathematics
Ulrich Daepp & Pamela Gorkin
Matrix Calculus (for Machine Learning and Beyond)
Alan Edelman, Steven G. Johnson
Non-Convex Optimization for Machine Learning
Prateek Jain & Purushottam Kar
Combinatorial Optimization: Theory and Algorithms
Bernhard Korte & Jens Vygen
Algorithms and Combinatorics (2)
Proofs and Refutations: The Logic of Mathematical Discovery
Imre Lakatos
Information Theory, Inference and Learning Algorithms
David J. C. MacKay
How to Prove It: A Structured Approach
Daniel J. Velleman
Filtering and System Identification: A Least Squares Approach
Michel Verhaegen & Vincent Verdult
Entropy and Diversity: The Axiomatic Approach
Tom Leinster
The Design of Approximation Algorithms
David P. Williamson & David B. Shmoys
Computer Vision: Models, Learning, and Inference
Simon J. D. Prince
Algorithms for Convex Optimization
Nisheeth K. Vishnoi