Machine Learning Notes 🤖📚

Welcome to the Machine Learning Notes repository! Here you'll find comprehensive notes on various topics covered in the Machine Learning course of the Master Degree program at the University of Trento. Whether you're a student studying for exams or someone eager to dive into the world of machine learning, you're in the right place! 🎓

In this updated version, I've added additional content, including a new chapter, and fixed all typographical errors to provide you with the best learning experience possible. 🚀

Contents 📋

1. Introduction to Machine Learning 🤖
2. Decision Trees 🌳
3. K-nearest Neighbors 🏘️
4. Linear Algebra ➗
5. Probability Theory 🎲
6. Evaluation ✔️
7. Parameter Estimation 🔍
8. Bayesian Networks 🔄
9. Inference in BN 🧮
10. Learning BN 📖
11. Naive Bayes 🤞
12. Linear Discriminant Functions ➡️
13. Support Vector Machines 🛠️
14. Non-linear SVMs 🔄
15. Kernel Machines ⚙️
16. Deep Learning 🧠
17. Ensemble Methods 🎭
18. Unsupervised Learning 🧩
19. Reinforcement Learning 🎮

Download 📥

To download a PDF version of these notes, click on the image below:

Machine Learning Notes

Feel free to explore, learn, and contribute to this repository! Let's dive into the fascinating world of machine learning together! 🤗💻