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 Cesa-Bianchi |
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 Cesa-Bianchi |
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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 Shalev-Shwartz e Shai Ben-David, 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, human-computer 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.
Disclaimer
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. If you are interested in the practical aspects, please check out the other courses in the Predictive Modeling and Data Science track of the MSc in Computer Science.
Syllabus
Topics will not be necessarily taught in this order. English versions of lecture notes will be made available.
Exams
The exam consists in writing a paper of about 10-15 pages containing either a report describing experimental results (experimental project) or a in-depth analysis of a theoretical topic (theory project). The paper will be discussed in an oral examination, in which students will be asked detailed questions about the algorithms used in the project, and also more high-level questions on the rest of the syllabus.
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 real-world 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 paper will be preferably written in latex and contain images and plots to illustrate the experimental results. The description of the methodology and the algorithms must be detailed enough to allow reproducibilty of the results. The paper may be structured as follows
The theory project is typically (but not exclusively) focused on a topic taught in class. The report will be based on one or more scientific papers (provided by the instructor), and must contain the complete proof of at least a technical result, including all necessary definitions and auxiliary lemmas. The paper 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.