Machine Learning and Pattern Recognition Labs

This repository contains the laboratory exercises for the Machine Learning and Pattern Recognition (MLPR) course at Politecnico di Torino.

Repository Structure

• Lab3: Classification, PCA and LDA
• Lab4: Clustering
• Lab5: Gaussian Density
• Lab6: Gaussian Classifiers (MVG, Naive Bayes, Tied)
• Lab7: Language Models
• Lab8: Text Classification
• Lab9: Optimization and Bayes Risk
• Lab10: Advanced Bayes Risk
• Lab11: Bayes Risk Continuation
• Lab12: Gaussian Mixture Models (GMM)
• Lab13: Logistic Regression and Evaluation

Content

Each laboratory directory contains:

• PDF file with laboratory instructions
• Python implementation code
• Test data and solutions (when available)
• Support files and utilities

Usage

To run the Python code, make sure you have installed:

• Python 3.x
• NumPy
• Matplotlib (for visualizations)
• Other dependencies specified in individual laboratories

Author

Marco Donatucci - Student at Politecnico di Torino