A detailed collection of LaTeX-based lecture notes for the Machine Learning course at the University of Trento, academic year 2025/2026, instructed by Prof. Andrea Passerini.
The notes cover fundamental and advanced topics in Machine Learning.
| Section | Description |
|---|---|
| Evaluation | Performance measures for classifiers: accuracy, precision, recall, F-measure, confusion matrices (binary & multi-class), K-Fold cross-validation |
| Decision Trees | Tree-based classifiers, splitting criteria, pruning, Random Forests |
| K-Nearest Neighbours | Instance-based learning, distance metrics |
| Parameter Estimation | MLE, MAP, Bayesian estimation, i.i.d. assumptions |
| Bayesian Networks | DAG structure, independence maps, d-separation, factorization |
| Learning Bayesian Networks | Parameter learning, structure learning from data |
| Naive Bayes | Parameter learning, conditional independence assumption |
| Linear Discriminant Functions | Discriminant vs Generative models, Linear classifiers, decision boundaries |
| Perceptron | Single-layer perceptrons, learning algorithm |
| Support Vector Machines | Maximum margin classifiers, soft margin, dual formulation |
| Kernel Machines | Feature maps, Kernel trick, valid kernels, Gram matrix, Kernel on graphs |
| Neural Networks | Multiple Layer Perceptrons, backpropagation, activation functions, regularization, other modern architectures |
| Unsupervised Learning | Clustering, K-Means, EM-Clustering, Hierarchical Clustering, choosing n. of clusters |
| Unsupervised Learning | Clustering, K-Means, EM-Clustering, Hierarchical Clustering, choosing n. of clusters |
| Reinforcement Learning | Markov Decision Processes, Utilities, Value Iteration, Policy Iteration, Unknown MDPs, ADP, TD Learning, SARSA, Q-Learning |
Contributions are welcome! Here's how you can help:
- Found a typo, a wrong formula, or a missing topic? Open an issue.
- Please include the section name and page number (if applicable).
- Fork this repository.
- Create a new branch:
git checkout -b fix/your-description. - Make your changes (edit the
.texfiles undersections/). - Make sure the project compiles without errors:
latexmk -pdf main.tex. - Commit with a descriptive message and push your branch.
- Open a Pull Request against
main.
This project is intended for educational purposes. Feel free to use and share the notes with proper attribution.