95662 - INTRODUCTION TO MACHINE LEARNING

Learning outcomes

At the end of the course, students will have gained a comprehensive understanding of key machine learning techniques. They will have the skills to effectively apply and adapt these methods across diverse situations. Additionally, students will be equipped with fundamental knowledge of the probabilistic concepts underpinning these methods.

Course contents

- Introduction to machine learning. Regression and classification problems. Supervised, unsupervised and reinforcement learning. Overfitting and regularization.

- Foundations of information theory.

- Linear models for supervised learning.

- Unsupervised learning: clustering, latent models, matrix factorization.

- Probabilistic models.

- Neural networks, backpropagation, stochastic gradient descent.

- Deep learning, convolutional networks, transformer.

- Language models.

- Selection of advanced topics: kernel and diffusion models, large language models, AlphaGo, AlphaTensor.

Readings/Bibliography

- Christopher Bishop, Pattern Recognition and Machine Learning

- Aston Zhang, Zack C. Lipton, Mu Li, Alex J. Smola, Dive into Deep Learning

- Christopher Bishop, Deep Learning

- Richard S. Sutton and Andrew G. Barto, Reinforcement Learning: An Introduction

- Dan Jurafsky and James H. Martin, Speech and Language Processing

Teaching methods

- Frontal lectures on the board and/or with slides.

- Coding and simulation activities in the laboratory.

Assessment methods

Submission of a final group project in Python, followed by an oral interview aimed at verifying the individual contributions of each student.

Teaching tools

- Office hours and tutoring.

- PDF lecture notes covering some parts of the program.

- Coding sessions supervised by a tutor.

Office hours

See the website of Stefano Pagliarani

See the website of Giovanni Paolini