81610 - Machine Learning

Course Unit Page

Academic Year 2021/2022

Learning outcomes

At the end of the course, the student has an understanding of theoretical foundations, computational properties, and use cases for some of the most popular supervised and unsupervised machine learning techniques. In particular, the student is able to address tasks such as classification, clustering, and discovery of rules by using modern machine learning methods and libraries.

Course contents

The course starts with an introduction to Deep Learning, Neural Networks and their typical learning mechanism: the backpropagation algorithm.

We shall discuss the main types of neural nets: feed forward,
convolutional and recurrent, providing concrete examples and discussing successful neural architectures for image processing, localization, segmentation, style transfer, text processing and many other tasks.

We shall investigate techniques to visualize the behavior of hidden units (tightly related to deep dreams and inceptionism), techniques to fool neural networks, and modern generative approaches comprising Variational Autoencoders, Generative Adversarial Networks, and their most recent developments.

The final part of the course will be devoted to an introduction to Deep Reinforcement Learning, with applications to the design of intelligent agents for video games, autonomous driving and other situations requiring complex and adaptive behaviors.


Suggested reading:

I.Goodfellow, Y.Bengio, A.Courville Deep Learning MIT Press.


Specific pointers to on line material will be provided at each lesson, in addition to the slides of the course.

Teaching methods

Frontal lessons based on slides, with discussion of practical examples via pyhton notebooks. Teaching methods may be subject to variation due to the Corona Virus emergency.

Assessment methods

Home project on a restricted set of topics selected by the teacher.

Projects can be done in teams of up to two members. The exam will consist in the presentation and discussion of the homework. 

The assesment method may change in relation with the attendance.

Teaching tools

Lectures will make extensive usage of slides. Working examples will be delivered by means on python notebooks.

Office hours

See the website of Andrea Asperti