OVERVIEW

The course introduces the main problems in machine learning, both supervised and unsupervised, along with the algorithms used to solve them. The aim is to provide a solid understanding of fundamental definitions, the statistical properties of the algorithms, and the computational techniques involved. Through lab sessions, students will experiment with practical implementations on real and synthetic data, and will be encouraged to interpret results critically, thus fostering the development of analytical skills and methodological independence.

AIMS AND CONTENT

LEARNING OUTCOMES

Provide the tools for theoretical understanding and practical use of the main supervised and unsupervised learning algorithms.

AIMS AND LEARNING OUTCOMES

At the end of the course, the student will have:

• a good understanding of the basic notions of machine learning and of the related basic mathematical tools; 
• a comprehension  of the basic concepts and techniques of convex optimization
• a good knowledge of the statistical and computational  properties of some well known machine learning   algorithms;

Thanks to the lab sessions, at the end of the course, the student will have: 

• some ability to implement machine learning algorithms on synthetic and real data sets.

PREREQUISITES

Linear algebra, basic knowledge of univariate and multivariate calculus. Some knowledge of probability theory.

TEACHING METHODS

Classes using  blackboard  to introduce the theorethical concepts and the main statistical learning algorithms. Lab activities in parallel to experience how the proposed methods work in practice. Students with certified learning disabilities (DSA) are invited to contact the instructor and the disability support officer to arrange any individualized teaching methods.

SYLLABUS/CONTENT

The teaching will offer an introduction to the main tools which are necessary to understand statistical learning,  and a number of supervised learning algorithms, such as local methods, regularization networks, linear and non linear models. The Course will also give a basic introduction to neural networks. Unsupervised problems such as clustering and dimensionality reduction will also be treated.  All the methods covered in the course will be implemented and during the lab sessions. 

The teaching will contribute to the following objectives and goals for the  Agenda 2030 for sustainable development:

• Goal 4. Ensure inclusive and equitable quality education and promote lifelong learning opportunities for all
• Goal 5. Achieve gender equality and empower all women and girls

RECOMMENDED READING/BIBLIOGRAPHY

• L. Rosasco, Introductory Machine Learning Notes, University of Genoa,  (http://lcsl.mit.edu/courses/ml/1718/MLNotes.pdf)
• Steinwart, Ingo, Christmann, Andreas, Support vector machines, Springer, ISBN 978-0-387-77241-7
• Cucker, Felipe, Zhou, Ding-Xuan, Learning theory: an approximation theory viewpoint, Cambridge University Press 2007, ISBN  978-0-521-86559-3

• Hastie, Tibshirani and Friedman. Elements of statistical learning

• Shalev-Shwartz and Ben-David. Understanding Machine Learning: from Theory to Algorithms

• Boyd, Vandenberghe, Convex Optimization, Cambridge University Press, 2004, ISBN 0 521 83378 7

TEACHERS AND EXAM BOARD

LESSONS

LESSONS START

In agreement with the official academic calendar

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

There are two examination options.

The first consists of written (and laboratory) mid-term tests, which involve the application of the concepts introduced during the course. The final grade is calculated as the average of the lab report evaluations and the mid-term tests (provided all three evaluations are satisfactory). Students may choose to complete the exam with an oral test or keep the grade obtained from the written tests and laboratory work. If any of the lab reports are unsatisfactory, they must be corrected and resubmitted. If one of the written tests is unsatisfactory, the student may choose either to take an oral test on the failed part or to take an oral exam covering the entire syllabus.

The second option consists of a single oral exam at the end of the course covering the entire syllabus, in addition to the submission of all lab reports.

Students with certified learning disabilities (DSA), disabilities, or other special educational needs are advised to contact the instructors at the beginning of the course to arrange teaching and examination methods that, while respecting the learning objectives, take into account individual learning needs and provide appropriate compensatory tools.

ASSESSMENT METHODS

The written  and the oral exam contain exercises and theoretical questions on the topics covered by the teaching, and will require the comprehension and the ability to use the introduced concepts and algorithms. The lab exam will be a series of reports based on the guided  implementation and  use of the algorithms introduced in theoretical classes (notebooks will be used). 

FURTHER INFORMATION

• Ask the professor for other information not included in the course description.