- Results of the September 25 written test are available here.
- Only for DSE students: before taking the written test on September 25 2023, please kindly register through this form by September 22. Students who do not register by this deadline will not be admitted to the written test.
- Clarification on how the final grade is computed: 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.
- Preliminary quiz list for the written tests available here. Version of June 8, 2023.
- Only students who are registered to the corresponding exam session will be admitted to the written test.
- For the past edition of this course, follow this link.
For students of the MSc in Data Science for Economics
- The course Machine learning and statistical learning has two separate exams, one for the MACHINE LEARNING module (Cesa-Bianchi, 40 hours, this course) and one for the STATISTICAL LEARNING module (Salini, 40 hours).
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 (2nd edition), MIT Press, 2018.
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.
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.
- Introduction (version of February 27, 2023)
- The Nearest Neighbour algorithm (version of March 6, 2023)
- Tree predictors (version of March 13, 2023)
- Statistical learning (version of March 27, 2023)
- Risk analysis for tree predictors (version of March 21, 2023)
- Hyperparameter tuning and risk estimates (version of March 21, 2023)
- Consistency and nonparametric algorithms (version of March 30, 2023)
- Risk analysis for Nearest Neighbour (version of May 31, 2023. Small typo fixed.)
- Linear predictors (version of April 22, 2023)
- Online gradient descent (version of May 31, 2023. Flipped sign in the def of hinge loss fixed.)
- Kernel functions (version of May 16, 2023)
- Support Vector Machines (version of May 23, 2023)
- Stability and risk control for SVM (version of May 26, 2023)
- Neural networks and deep learning (version of June 1, 2023)
- Logistic regression and surrogate loss functions (version of June 8, 2023)
- Boosting and ensemble methods (optional, not covered in class)
Notebooks shown in class will be added here.
Experimental projects for students who attended the course in the academic year 2022-23 or earlier:
Warning: The current list of projects is valid until the end of May 2024. This is the only deadline. Projects turned in after May 31, 2024 will be ignored
- Regular projects
- Joint project with the course Algorithms for massive datasets (this option is offered only to students attending the MSc in Computer Science, where both courses can be taken simultaneously)
The exam consists of two parts:
Fill out this form to turn in the project
- 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).
- Taking a written test on all the topics covered in class.
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 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 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., Keras). The report, preferably written using LaTeX, will be evaluated according to the following criteria:
It is OK if two students submit an experimental project together as a group. Clearly, such group projects are expected to be more extensive than single-person projects (more experiments, comparison with baseline algorithms, etc).
- Correctness of the general methodological approach
- Reproducibility of the experiments
- Correctness of the approach used for choosing the hyperparameters
- Clarity of exposition
If your solution is adapted from other sources (e.g., Kaggle), this must be clearly stated, and the report should explain the differences and compare the experimental results.
Steps to complete the experimental project:
- Fill out a form to choose a project
- Create a public repository containing both the code and the report (in pdf)
- Fill out a form to turn in the project
- In 1-2 weeks, you will receive the grade via email.
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 in-depth presentation of the paper's results, including its connections with the related literature. The report may be structured as follows
Note: The theory project report MUST be written in LaTeX.
- Introduction and description of the problem
- Most important related works
- Notation and relevant definitions
- Proof of a technical result
- Some critical considerations.
Steps to complete the theory project:
IMPORTANT FOR ALL PROJECTS:
Your report must contain the following declaration: I/We declare that this material, which I/We now submit for assessment, is entirely my/our own work and has not been taken from the work of others, save and to the extent that such work has been cited and acknowledged within the text of my/our work. I/We understand that plagiarism, collusion, and copying are grave and serious offences in the university and accept the penalties that would be imposed should I engage in plagiarism, collusion or copying. This assignment, or any part of it, has not been previously submitted by me/us or any other person for assessment on this or any other course of study.
- Send an email directly to the instructor to agree on a topic;
- Turn in the report via email to the instructor;
- After 1-2 weeks, the instructor will get in touch to agree on a date for the oral discussion.
Browse the calendar pages and click on a day to find out what was covered on that day.