Technical Spring 2025

Database systems are used for storing and maintaining any sort of data, providing convenient access to them through efficient query processing.

This course aims to give fundamental knowledge about databases, with particular attention to relational models. Students will learn how to design a relational database, using established methods such as E/R diagrams, and implement them. In addition, this course will offer an extensive presentation of SQL query languages.

Teaching and Learning Methods:

Lectures (with in-class exercises) and individual practical labs.

Since 1948, the year of publication of Shannon’s landmark paper “A mathematical theory of communications”, Information theory has paved the ground for the most important developments of today’s information/communication world making it perhaps the most important theoretical tool to understand the fundamentals of information technologies. The main objective of this course is to provide an introductory-level coverage of Information Theory. Information theory studies the ultimate theoretical limits of source coding and data compression, channel coding, and reliable communications via channels, and provides the guidelines for the development of practical signal-processing and coding algorithms. The course is meant to provide an intuitive point of view and foundation for both research and practice.

Teaching & Learning Methods: Lectures and homework.

Course policies: 

Attendance Policy: Attendance is expected and required in every class period, unless previously discussed with the instructor. We will cover a lot of ground in this course to build a strong foundation for Information Theory, so attendance is important. To encourage learning, preparation, and participation, the lectures will be complemented by additional reading or review materials, e.g., slides, papers, MATLAB codes, and online course materials to cover some of the basic concepts prior to the lectures.

Other Course Policies: All mobile devices (e.g., smartphones, tablets, computers) should be stored securely away during lecture and are not be used unless specifically directed otherwise by the instructor. Use of a mobile device during an exam without the explicit permission of the instructor will be interpreted as the illicit transfer of exam data, will be considered an act of cheating and will be treated as such.

You are expected to approach the instructor with any issue that may affect your performance in class ahead of time. This includes absence from important class meetings, late assignments, inability to perform an assigned task, the need for extra time on assignments, etc. You should be prepared to provide sufficient proof of any circumstances based on which you are making a special request.

Academic Integrity: Student-teacher relationships are built on trust. For example, students must trust that teachers have made appropriate decisions about the structure and content of the courses they teach, and teachers must trust that the assignments that students turn in are their own. Acts that violate this trust undermine the educational process. In this class, all assignments that are turned in for a grade must represent the student’s own work. Submission of any assignment that is in violation of this policy may result in a penalty of an F in the class and may be subject to further disciplinary action.

If you have any questions concerning this policy before submitting an assignment, please ask for clarification.

• This course provides a broad overview of computer networking, covering the application layer, transport layer, network layer, and link layers.
• It covers basic concepts in computer networking as well as the prominent Internet protocols.

Teaching and learning methods:

Lectures and Lab sessions (group of 2-3 students)

Course  Policies: attendance to labs is mandatory

The objective of this course is to raise awareness of the threats and challenges and to introduce the main concepts of cybersecurity. The aim is to present a guide of good practices applicable to all IT professionals, whether they are in training or in activity.

Statistics is a foundation of many areas of science and engineering that involve `` data.’’ This course focuses on fundamental concepts in statistical inference that are necessary for applying statistical methods in practice and that form the basis of other fields such as machine learning.

Teaching and Learning Methods:  Students learn by lectures, exercises, and computer experiments.

Course Policies:  The valid usage of statistical methods requires a mathematical understanding of the underlying mechanism. As such, the course covers both mathematical and algorithmic aspects of statistics.  

Deep Learning is a new approach in Machine Learning which allows to build models that have shown superior performance fora wide range of applications, in particular Computer Vision and Natural Language Processing. Thanks to the joint availability of large data corpus and affordable processing power, Deep Learning has revived the old field  of Artificial Neural Networks and provoked the "Renaissance" of AI (Artificial Intelligence). The objective of this course is to provide an overview of the field of Deep Learning, starting from simple Neural Network architectures and pursuing with contemporary and state of the art practices and models. The course is organized as lectures where the theory is exposed and discussed, whereas hands-on sessions (labs) are provided in the form of online tutorials.

Teaching and Learning Methods : The course is composed of a combination of lectures and online tutorials.

Course Policies : Attendance to all sessions is mandatory.

This course introduces fundamental concepts in machine learning with applications to networked systems and the Internet of Intelligent Things (IoIT). Students will gain foundational knowledge of cutting-edge methods, including autoencoders, deep generative models, and reinforcement learning. They will also get familiar with fundamental information-theoretic frameworks (e.g., information bottleneck) and theoretical principles. We will also introduce large-scale distributed and decentralized learning over wireless networks, in particular under constraints (completion time, radio resources, computational efficiency, etc.).

Teaching and Learning Methods: Lectures, exercise sessions, and lab sessions. Each lecture starts summarizing key concepts from the previous lecture. Part of each lecture is often dedicated to illustrative examples and exercises.

Course Policies: Attendance to lab session is mandatory. Attendance to lectures and exercise sessions is highly recommended..

This course treats the subject of modern radio engineering and includes typical RF architectures and their characterizations, modeling, prediction and simulation of radio-wave propagation, cellular planning, systems-level aspects of modern radio network design.

 Teaching and Learning Methods : Lectures and Lab sessions (group of 2 students)

 Course Policies : Attendance to Lab session is mandatory.

This course focuses on the principles of learning from data and quantification of uncertainty in the context of machine learning, by complementing and enriching the “Machine Learning and Intelligence Systems” course. The presentation of the material follows a common thread based on the probabilistic data modeling approach, so that many classical learning models/algorithms can be seen as special cases of inference problems for more general probabilistic models.

We will start by drawing connections between loss optimization and probabilistic inference (e.g., maximum likelihood estimation, maximum a posteriori estimation). We will then introduce the concept of Bayesian inference, and discuss how to perform inference in complex and intractable models using approximate methods (e.g., variational inference, Markov Chain Monte Carlo, Laplace approximation). We will then focus on prediction, and the evaluation of predictive models. We will start by discussing simple predictive models (e.g., linear regression, logistic regression), and then move on to more complex models (e.g., Gaussian processes, neural networks). We will discuss all these models in the context of probabilistic inference, and we will put in practice the probabilistic methods introduced in the first half. While mostly focused on supervised learning, we will also take a look at unsupervised learning and probabilistic generative models. Finally, the course will be complemented by several practical sessions, where students will be able to implement and experiment with the methods discussed in class (using Python).

Teaching and Learning Methods : Lectures and Lab sessions (preferably one student per group)

Course Policies : Attendance of Lab sessions is mandatory

Teaching and Learning Methods: Lectures, Homework (2-3), and a case study (study and present a research paper in a group of 2-3 students). 

Course Policies: Mandatory participation, Case study optional, but recommended.

This module addresses the access methods in Wireless Local Access Networks (WLAN). The basic contention and management mechanisms are detailed. Current and emerging standards of WLAN toward 5G are also presented.

Teaching and Learning Methods : Lectures and Lab sessions (group of 2 students)

Course Policies :  Attendance to Lab session is mandatory.