This book contains useful notes for students studying for an introductory course to Machine Learning, or to anyone interested in the subject. The structure of the book results from my notes during the "Introduction to Machine Learning" course held by Elisa Ricci at the University of Trento in the academic year 2024-2025. During the course, I found myself more interested in the math than what the course taught, so I dedicated some of my time to understand the math better and to fill some of the gaps in my education. My final notes resulted in the book you are reading.
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- Machine Learning Basics
- The K-nearest neighbors algorithm
- Linear Models
- Beyond Binary Classification
- Decision Trees
- Gradient descent
- Support Vector Machines
- Regularization
- Unsupervised learning
- Clustering
- Neural Networks
- Deep Generative Models
- Diffusion Models
- Reinforcement Learning
To generate the .tex file from the .md files:
./amalgamanted.sh
The following programs must be available in the $PATH:
- pandoc
- texi2pdf