statistical methods for machine learning
6 CFU, Laurea magistrale in Informatica (F94)
machine learning
6 CFU, MSc in Data Science and Economics
INSTRUCTOR/DOCENTE: Nicolò CesaBianchi

statistical methods for machine learning
6 CFU, Laurea magistrale in Informatica (F94)
machine learning
6 CFU, MSc in Data Science and Economics
INSTRUCTOR/DOCENTE: Nicolò CesaBianchi

News
For students of the MSc in Data Science and Economics
Bibliographic references:
Lecture notes provided by the instructor
The course makes heavy use of probability and statistics. A good textbook on these topics is:
Some good machine learning textbooks:Dimitri P. Bertsekas and John N. Tsitsiklis, Introduction to Probability (2nd edition). Athena Scientific, 2008.
Shai ShalevShwartz e Shai BenDavid, Understanding Machine Learning: From Theory to Algorithms, Cambridge University Press, 2014.Mehryar Mohri, Afshin Rostamizadeh e Ameet Talwalkar, Foundations of Machine Learning, MIT Press, 2012.
L. Devroye, L. Gyorfi, and G. Lugosi, A Probabilistic Theory of Pattern Recognition, Springer, 1996.
Goals
Machine learning is concerned with the design of algorithms that can predict the evolution of a phenomenon based of a set of observations. A standard tool in the development of intelligent systems, machine learning has been successfully applied to a wide range of domains, including vision, speech and language, humancomputer interaction, personalized recommendations, health and medicine, autonomous navigation, and many more. The course will describe and analyze, in a rigorous statistical framework, some of the most important machine learning techniques. This will provide the student with a rich set of methodological tools for understanding the general phenomenon of learning in machines.
Note: This is a course about the theoretical foundations of machine learning and the analysis of machine learning algorithms. The focus is on understanding the mathematical principles at the basis of machine learning.
Syllabus
Experimental projects for students who attended the course in the academic year 202122 or earlier:
Exams
The exam consists in writing a paper of about 1015 pages containing either a report describing experimental results (experimental project) or a indepth analysis of a theoretical topic (theory project).
For experimental projects:
Oral exam: after the project is evaluated, all students must take an oral exam where they will be asked detailed questions (mathematical definitions, technical proofs, description of algorithms) on the following contents.
IMPORTANT
The experimental project is typically based on implementing two or more learning algorithms (or variants of the same algorithm) from scratch. The algorithms are compared on realworld datasets. The programming language is immaterial. However, the implementation should be reasonable in terms of running time and memory footprint. If the experimental project is based on neural networks, then the student is allowed to use a toolbox (e.g., Tensorflow). The report, preferably written using LaTeX, will be evaluated according to the following criteria:
The theory project is typically (but not exclusively) focused on a topic taught in class. The report will be based on one scientific paper (provided by the instructor), and must contain the complete proof of at least a technical result, including all necessary definitions and auxiliary lemmas. The goal of the theory project is to provide an indepth presentation of the paper's results, including its connections with the related literature. The report may be structured as follows
Course calendar:
Browse the calendar pages and click on a day to find out what was covered on that day, and the link to the video in case the class was viderecorded.