statistical methods for machine learning
6 CFU, MSc in Computer Science
machine learning and statistical learning
6 CFU, MSc in Data Science for Economics
202324 edition

statistical methods for machine learning
6 CFU, MSc in Computer Science
machine learning and statistical learning
6 CFU, MSc in Data Science for Economics
202324 edition

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For students of the MSc in Data Science for 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 (2nd edition), MIT Press, 2018.
Goals
Machine learning is the main enabling technology of modern artificial intelligence. This course explains the statistical foundations of machine learning, describes some fundamental algorithms for supervised learning, and shows how to analyze their performance. Emphasis is on theory and principled methods as opposed to practice and heuristics.
Syllabus
Notebooks shown in class will be added here.
Exams
The exam consists of two parts:
After the experimental project is turned in and graded, in order for the mark to be validated the student must answer a few questions about the project (date and time to be agreed with the TAs).
The evaluation of the theory project also includes an oral exam on the report's contents and the related topics covered in class.
The written test can be only taken at the regular exam sessions. The project can be turned in at any time between June 2023 and the end of May 2024. The final grade is the arithmetic average (rounded to the nearest integer) of the mark obtained in the written test and the mark obtained in the project. The exam is passed if: the average is 18 or higher and both marks are 17 or higher.
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., Keras). The report, preferably written using LaTeX, will be evaluated according to the following criteria:
Steps to complete the experimental project:
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
Steps to complete the theory project:
Course calendar:
Browse the calendar pages and click on a day to find out what was covered on that day.