Course Information

The course is held on the second term. The schedule for A.Y. 2024/25 is provided in table below.

The first lecture of the course will be ON FEBRUARY 18th 2025 h. 11.00. The course will be hybrid, both in person and online on the dedicated MS Team.

Recordings of the lectures will be made available to the students following the course.

Objectives

Course Prerequisites

Course prerequisites include knowledge of machine learning fundamentals (e.g. covered through ML course). Knowledge of elements of probability and statistics, calculus and optimization algorithms are also expected. Previous programming experience with Python is a plus for the practical lectures.

Course Overview

The course introduces students to the analysis and design of advanced machine learning and deep learning models for modern pattern recognition problems and discusses how to realize advanced applications exploiting computational intelligence techniques.

The course is articulated in five parts. The first part introduces basic concepts and algorithms concerning traditional pattern recognition, in particular as pertains sequence and image analysis. The next two parts introduce advanced models from two major learning paradigms, that are deep neural networks and probabilistic models and their use in pattern recognition applications. The fourth part will cover generative deep learning and the intersection between probabilistic and neural models. The final part of the course will present selected recent works, advanced models and applications of learning models.

Presentation of the theoretical models and associated algorithms will be complemented by introductory classes on the most popular software libraries used to implement them.

The course hosts guest seminars by national and international researchers working on the field as well as by companies that are engaged in the development of advanced applications using machine learning models.

The official language of the course is English: all materials, references and books are in English. Lecture slides will be made available here, together with suggested readings.

Topics covered -Bayesian learning, graphical models, learning with sampling and variational approximations, fundamentals of deep learning (CNNs, AE, DBN, GRNs), deep learning for machine vision and signal processing, advanced deep learning models (transformers, foundational models, NTMs), generative deep learning (VAE, GANs, diffusion models, score-based models) deep graph networks, reinforcement learning and deep reinforcement learning, signal processing and time-series analysis, image processing, filters and visual feature detectors, pattern recognition applications (machine vision, bio-informatics, robotics, medical imaging, etc), introduction to programming libraries and frameworks.

Textbooks and Teaching Materials

We will use two main textbooks, one covering the parts about generative and probabilistic models, and the other covering the deep learning modules. Note that all books have an electronic version freely available online.

BOOKS

[BRML] David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press (PDF)

[SD] Simon J.D. Prince, Understanding Deep Learning, MIT Press (2023) (PDF)