Machine Learning Theory

• Mathematics for Machine Learning : Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong
• Knowledge Discovery with Support Vector Machines : Lutz H. Hamel
• Machine Learning: A Probabilistic Perspective : Kevin P. Murphy
• Convex Optimization : Stephen P. Boyd, Lieven Vandenberghe
• Nonlinear Programming : Peter Zörnig
• Statistical Rethinking : Richard McElreath
• Artificial Intelligence Engines : James Stone
• Probability for the Enthusiastic Beginner : David Morin
• The Geometry of Multivariate Statistics : Thomas D. Wickens
• Statistical Methods: The Geometric Approach : David J. Saville, Graham R. Wood

Machine Learning Practice

• Machine Learning Engineering: Andriy Burkov
• Machine Learning Design Patterns: Valliappa Lakshmanan, Sara Robinson, Michael Munn
• Matrix Computations: Gene H. Golub, Charles F. Van Loan

Distributed Systems

• Designing Data-Intensive Applications: Martin Kleppmann
• Elements of Distributed Computing: Vijay K. Garg
• Distributed Algorithms: A Verbose Tour: Fourré Sigs
• Streaming Systems: Tyler Akidau, Slava Chernyak, Reuven Lax
• Distributed Algorithms: Nancy Lynch

Programming and Architecture

• Enterprise Integration Patterns: Gregor Hohpe, Bobby Woolf
• Algorithms: Robert Sedgewick, Kevin Wayne
• Monolith to Microservices: Sam Newman
• Technology Strategy Patterns: Architecture as Strategy: Eben Hewitt

Mathematics

• Writing Proofs in Analysis: Jonathan M. Kane
• How to Think about Analysis: Lara Alcock
• Principles of Topology: Fred H. Croom
• The Way of Analysis: Robert S. Strichartz
• Functional Analysis: Joseph Muscat
• Introductory Functional Analysis with Applications: Erwin Kreyszig
• Functional Analysis for Physics and Engineering: Hiroyuki Shima
• Linear Algebra: Theory, Intuition, Code : Mike X Cohen
• Introduction to Linear Algebra : Gilbert Strang
• Vector Calculus, Linear Algebra and Differential Forms: A Unified Approach : John H. Hubbard, Barbara Burke Howard
• Multivariate Calculus and Geometry : Sean Dineen

Web Resources

• Principal Component Analysis as Optimisation: http://alexhwilliams.info/itsneuronalblog/2016/03/27/pca/
• Gaussian Processes: https://distill.pub/2019/visual-exploration-gaussian-processes/
• Statistical Machine Learning Theory: https://www.youtube.com/c/T%C3%BCbingenML/playlists
• Kernel Methods: https://www.youtube.com/channel/UCotztBOmGVl9pPGIN4YqcRw/videos
• Lagrange Multipliers: https://www.youtube.com/watch?v=5A39Ht9Wcu0