Machine Learning (AIS): Module I

General information

Degree: Master of Science in Artificial Intelligence Systems
Period: September - December


The course aims at studying the fundamentals of machine learning, covering supervised and unsupervised learning methods. It includes application examples as well as laboratory exercises. At the end of the course, it is expected that the student will acquire the skills useful to design methods and tools for the analysis of signals and data.

Prerequisites (suggested)

Linear algebra, probability theory. Boolean algebra, knowledge of a programming language. For a good introduction to linear algebra see: Gilber Strang, Introduction to Linear Algebra, Wellesley-Cambridge Press, 2016.


The second part of module I (taught by me) covers the following topics: linear Discriminant Analysis (perceptron, least-square regression, SVM); kernel machines (kernel methods, kernels, kernels on structures); learning Bayesian networks (parameter learning, structure learning); reinforcement learning; unsupervised learning.

Course Information (part taught by me)

Instructor: Andrea Passerini
Teaching assistant: Antonio Longa
Office hours: Arrange by email
Lecture time: Monday 11:30-13:30 (room a102)
Wednesday 9:30-11:30 (room a102)
Communications: Please check the moodle page of the course for news and updates.
Bibliography: R.O. Duda, P.E. Hart and D.G. Stork, Pattern Classification (2nd edition), Wiley-Interscience, 2001.
D. Koller and N. Friedman, Probabilistic Graphical Models, The MIT Press, 2009
J.Shawe-Taylor and N. Cristianini, Kernel Methods for Pattern Analysis, Cambridge University Press, 2004.
I. Goodfellow, Y. Bengio and A. Courville, Deep Learning, The MIT Press, 2016 (online version available here).
K. Murphy, Probabilistic Machine Learning: An Introduction, The MIT Press, 2021 (online version available here).
Slides: Linear discriminant functions [slides] [handouts]
Support Vector Machines [slides] [handouts]
Non-linear Support Vector Machines [slides] [handouts]
Kernel Machines [slides] [handouts]
Scikit-learn lab [slides] [material]
Unsupervised learning [slides] [handouts]
Reinforcement learning [slides] [handouts]
Parameter estimation [slides] [handouts]
Bayesian Networks [slides] [handouts]
Learning BN [slides] [handouts]
Naive Bayes [slides] [handouts]
Bayesian Network lab [slides] [data] [software]
Videos: Registered lectures (from previous year) made available on Moodle
Additional material: Linear algebra [slides] [handouts]
Probability theory [slides] [handouts]


Modality: Oral examination.