Recommended Books, Papers, and Video Lectures on Mathematics for AI and Machine Learning

Recommended Books, Papers, and Video Lectures on Mathematics for AI and Machine Learning

For those looking to dive deeper into the mathematical aspects of AI and machine learning, here are some recommended resources that cover various topics and levels of difficulty:

  1. 📖 Algebra, Topology, Differential Calculus, and Optimization Theory for Computer Science and Machine Learning by Jean Gallier and Jocelyn Quaintance
    • Includes mathematical concepts for machine learning and computer science.
    • Book Link
  2. 📖 Applied Math and Machine Learning Basics by Ian Goodfellow, Yoshua Bengio, and Aaron Courville
    • Covers math basics for deep learning from the Deep Learning book.
    • Chapter Link
  3. 📖 Mathematics for Machine Learning by Marc Peter Deisenroth, A. Aldo Faisal, and Cheng Soon Ong
    • A great starting point with examples and clear notation explanations.
    • Book Link
  4. 📖 Probabilistic Machine Learning: An Introduction by Kevin Patrick Murphy
    • A comprehensive overview of classical machine learning methods and principles.
    • Book Link
  5. 📖 Mathematics for Deep Learning by Brent Werness, Rachel Hu et al.
    • Covers mathematical concepts to help build a better understanding of deep learning.
    • Chapter Link
  6. 📖 The Mathematical Engineering of Deep Learning by Benoit Liquet, Sarat Moka, and Yoni Nazarathy
    • A concise overview of deep learning foundations and mathematical engineering.
    • Book Link
  7. 📖 Bayes Rules! An Introduction to Applied Bayesian Modeling by Alicia A. Johnson, Miles Q. Ott, and Mine Dogucu
    • A great online book that covers Bayesian approaches.
    • Book Link

📄 Papers

  1. The Matrix Calculus You Need For Deep Learning by Terence Parr & Jeremy Howard
    • A guide to understanding the fundamental matrix operations for deep learning.
    • Paper Link
  2. The Mathematics of AI by Gitta Kutyniok
    • A summary of the importance of mathematics in deep learning research.
    • Paper Link

🎥 Video Lectures

  1. Multivariate Calculus by Imperial College London
    • Covers fundamental matrix operations, the chain rule, and gradient descent.
    • Video Playlist
  2. Mathematics for Machine Learning – Linear Algebra by Imperial College London
    • Explains the role of linear algebra in neural networks and data transformations.
    • Video Playlist
  3. CS229: Machine Learning by Anand Avati
    • Lectures containing mathematical explanations of various machine learning concepts.
    • Course Link