OVERVIEW

The course  offers an introduction to the main machine learning algorithms, by covering the modeling and computational aspects.

AIMS AND CONTENT

LEARNING OUTCOMES

The primary objective is to provide the students with the basic language and tools of machine learning, with particular emphasis on the supervised case. The approach is based on a formulation of the problem of machine learning as an inverse stochastic problem. The students will also need to know some of the best known algorithms, including both statistical and computational properties.

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

PREREQUISITES

Calculus for functions of sesveral variables, probability and linear algebra. 

TEACHING METHODS

Classes using  blackboard and lab activities

SYLLABUS/CONTENT

The course 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. 

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

Exam Board

SILVIA VILLA (President)

CESARE MOLINARI

LORENZO ROSASCO (President Substitute)

ERNESTO DE VITO (Substitute)

LESSONS

LESSONS START

In agreement with the offical academic calendar

Class schedule

MACHINE LEARNING

EXAMS

EXAM DESCRIPTION

To pass the exam the student have to present a short report. The student can choose one among the following options:

1. analyze and discuss a research article on themse close to the ones studied during classes
2. implement an algorithms presented during the classes (in some programming language)
3. use some available code to analyze synthetic and/or real datasets and discuss the obtained results.

The topic studied in the report must be decided in advance in agreement with the instructors.

Students with DSA certification ("specific learning disabilities"), disability or other special educational needs are advised to contact the teacher at the beginning of the course to agree on teaching and examination methods that, in compliance with the teaching objectives, take account of individual learning arrangements and provide appropriate compensatory tools.

ASSESSMENT METHODS

The report preparation and its discussion are aimed at verifying the student's achievement of an independent critical reasoning capability  in the context of machine learning.

During the report presentation, basic questions d on the topics covered by the course amy be asked.