Machine Learning (AIS): Module I
General information
| Degree: | Master of Science in Artificial Intelligence Systems |
| Period: | September - December |
Objectives
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.
Content
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 Email:  |
| Teaching assistant: |
Emanuele Marconato Email: emanuele.marconato@unitn.it Giovanni De Toni Email: giovanni.detoni@unitn.it |
| Office hours: |
Arrange by email |
| Lecture time: |
Monday 11:30-13:30 (room a204) Wednesday 9:30-11:30 (room a104) |
| 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. S. Russell and P. Norvig,Artificial Intelligence: A Modern Approach, 4th Global ed., Pearson, 2021. K. Murphy, Probabilistic Machine Learning: An Introduction, The MIT Press, 2021 (online version available here). |
| Slides: |
Parameter estimation [slides] [handouts] Bayesian Networks [slides] [handouts] Learning BN [slides] [handouts] Naive Bayes [slides] [handouts] Bayesian Network lab [slides] [data] [software] Linear discriminant functions [slides] [handouts] Support Vector Machines [slides] [handouts] Kernel Machines [slides] [handouts] Scikit-learn lab [repository] Ensemble Methods [slides] [handouts] Unsupervised learning [slides] [handouts] Unsupervised learning Lab [repository] Reinforcement learning [slides] [handouts] Reinforcement learning Lab [repository] |
| Videos: |
Registered lectures (from previous year) made available on Moodle |
| Additional material: |
Linear algebra [slides] [handouts] Probability theory [slides] [handouts] |
Exams
| Modality: | Oral examination. |