OVERVIEW

The goal of this course is to provide an overview of classical Machine Learning algorithms, discussing modelling and computational aspects.

AIMS AND CONTENT

LEARNING OUTCOMES

Learning how to use classical supervised and unsupervised machine learning algorithms by grasping the underlying computational and modeling issues.

AIMS AND LEARNING OUTCOMES

At the end of the course, students will be able to:

UNDERSTAND and use the basic machine learning and statistical learning tools, considering supervised approaches, such as local methods, regularized methods with linear and non-linear models, and neural networks

UNDERSTAND and use unsupervised learning approaches such as clustering and dimensionality reduction. 

UNDERSTAND how to effectively set-up machine learning pipelines

IMPLEMENT the learning algorithms presented in the course

DEVELOP the ability to critically analyze analytical results

PREREQUISITES

Basic probability, calculus, linear algebra, programming.

TEACHING METHODS

Theoretical classes will be coupled with practical lab sessions
Occasionally, students will be asked to work in groups (for code development and analysis, for instance)

SYLLABUS/CONTENT

The course will cover the following topics:

• Machine Learning basics
• Empirical risk minimization
• Local methods
• Bias and Variance and K-Fold Cross Validation
• Regularized networks with linear models
• Feature maps and kernels
• Neural Networks
• Convolutional Neural Networks (basics)
• Clustering
• Dimensionality reduction

RECOMMENDED READING/BIBLIOGRAPHY

The material will provided by the instructors (slides and papers) and shared on the Aulaweb page of the course.

TEACHERS AND EXAM BOARD

Exam Board

NICOLETTA NOCETI (President)

ALESSANDRO VERRI

LORENZO ROSASCO (President Substitute)

LESSONS

LESSONS START

In agreement with the calendar approved by the Degree Program Board of Computer Science.

Class schedule

The timetable for this course is available here: Portale EasyAcademy

EXAMS

EXAM DESCRIPTION

The exam will be in written form and consist of theoretical questions and more practical exercises.
The students will have the possibility of opting for a reduced version of the written exam by submitting two mid-term assignments (consisting of a practical lab activity in Python)

ASSESSMENT METHODS

The exam will evaluate the overall understanding of Machine Learning basics, the capability to generalize the concepts to unseen problems and analyse the obtained results.
Clarity of exposition, completeness of the concepts, quality of the proposed solutions and critical thinking will be taken into account.

Exam schedule