Resource: The book can be download for free from the book’s website .

Book: Mathematics for Machine Learning

▲¹ This is the book that I’ll be reading, and working through, this summer (2025).

⠀⠀⠀⠀⠀ I’m a mathematics undergraduate, and like everyone else, I’ve been affected by the Ai hype. The utility of Large Language Models (LLM) is not up for debate. I would even dare to say that LLMs represent a paradigm shift on a scale comparable with the Internet itself. People all over the World are quickly abandoning search-based information retrieval (Google) in favor of a Ai(LLM) user interaction. It’s quicker and more pleasant than endlessly scrolling search engine results.

⠀⠀⠀⠀⠀ Assuming that LLMs represent a paradigm shift, it makes sense to gain a strong familiarty with how they work under the hood. However, that requires a pretty solid grounding in a few areas of mathematics: Linear Algebra, Multivariate Calculus, and Probability. That’s the reason why I’ve decide to spend this summer studying a book that provides a grounding in these three areas. But which book should I read?

⠀⠀⠀⠀⠀ Having asked on Reddit and read a few reviews, I decide do go with the book by Deisenroth, Faisal, and Ong. The book is free to download from the book’s website. I lucked out and found a physical copy at a local library. The only thing left to do is to start reading the book.

Discord: I have also created a Discord server that will function as waterhole for anyone that is interested in joining me in reading and studying this book over the summer.
You can find a post about it on Reddit.

Summer Reading Progress Table

The book has 12 chapters. I have decided to try and read one chapter per week, but I might not be able to reach that goal. We’ll see. Regardless, I’ll be tracking my weekly reading progress using this table. Every day, in the coming weeks, I will blog about the progress I make, and I will also do as many exercises as possible. These I hope to store in a repo on GitHub.

Chapter	Focus	Deadline
1	Introduction	✅ 2025-06-23
2	Linear Algebra	⬜ 2025-06-29
3	Analytic Geometry	⬜ 2025-07-06
4	Matrix Decompositions	⬜ 2025-07-13
5	Vector Calculus	⬜ 2025-07-20
6	Probability and Distributions	⬜ 2025-07-27
7	Continuous Optimization	⬜ 2025-08-03
8	When Models Meet Data	⬜ 2025-08-10
9	Linear Regression	⬜ 2025-08-17
10	Dimensionality Reduction with Principal Component Analysis	⬜ 2025-08-24
11	Density Estimation with Gaussian Mixture Models	⬜ 2025-08-31
12	Classification with Support Vector Machines	⬜ 2025-09-07

▲ These are the chapters in Mathematics for Machine Learning.