CSE 106-Resources

Textbooks and Lecture Notes:

• 1. An Introduction to Optimization with applications to Machine Learning, Edwin Chong. Wu-Sheng Lu, and Stanislaw Zak, Wiley, 5th edition.; free online (older) version, Amazon

  2. Linear Algebra and Optimization for Machine Learning, Charu Aggarawl, 2024

Extra resources:

• • The mathematics for machine learning, Diesenroth, Faisal, and Ong

  • Understanding Machine Learning: From Theory to Algorithms; Shai Shalev-Shwartz and Shai Ben-David; free online version;  Amazon

  • Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville,  Amazon

  • Calculus and optimization

    1. Calculus, Linear Algebra, and Differential Forms,  Hubbard and Hubbard; online version (perhaps an older edition)   Amazon, Cornell website

    2. Advanced Calculus, Loomis and Sternberg

  • Vector Calculus, Marsden and TrombaAn Introduction to Optimization, Edwin Chong and Stanislaw Zak, Wiley, 4th edition.

  • The Science of Deep Learning by Iddo Drori,  Parts of the book is available at the author's website

  • Patterns, Predictions, and Actions (mlstory.org) by Mortiz Hardt and Benjamin Recht; Amazon

  • Geometry of Deep Learning: A Signal Processing Perspective by Jong Chul Ye; Amazon

  • Convex Optimization - Algorithms and Complexity by Sebastien Bubeck; PDF from author's web page

  • Dive into Deep Learning — Dive into Deep Learning 1.0.3 documentation (d2l.ai) by Aston Zhang and Zachary Lipton

  • Neural Networks and Deep Learning: A Textbook by Charu Aggarwal; Amazon

  • Neural networks and deep learning by Michael Nielsen

  • Linear Algebra and Learning from Data (mit.edu) by Strang

  • Neural Network Learning (cambridge.org)  by Anthony and Bartlett

  • Optimization and Randomization