Computer Science

This course covers basic concepts of database management, database applications and database processing. Topics on various aspects of database programming, database design using the ER model, relational database design theory, application development will be covered. Query processing techniques will also be covered. Some other topics related to database processing may also be covered. 

This course gives an in-depth study in basic methods and practical tools for basal language technology (methods for automatic analyzation of language-based data). It covers both rule-based techniques, such as phrase structure grammar, and approximations with a starting point in machine learning, such as vector space semantics and classification. The course takes a look at some applications of methods for issues within language technology such as tagging, parsing, and text classification (such as sentiment analysis). The course has a strong practical component, with use of relevant tools and projects with written reports, among other things, which are required to qualify for the exam.

This course explores advanced mathematical problems and theoretical approaches in deep learning with a strong emphasis on privacy-related challenges. Key topics include: Differential privacy, with a focus on its application in federated learning and mechanisms to ensure robust privacy guarantees in distributed settings; Privacy in generative diffusion models, including the use of stochastic differential equations and innovative techniques to safeguard private data in generative processes; Privacy considerations in large language models (LLMs), examining methods for mitigating data leakage, adversarial attacks, and ensuring compliance with differential privacy principles in training and inference.

Learn the basic concepts of various different computer languages (e.g., C, Python)

Learn how to write programs using different computer languages

Learn how to solve computational problems using programming

This course covers solving problems using algorithmic thinking with the concept of "object-oriented" programming. Students will learn to express algorithms in English, then translate them into the programming language using Python, C++. Topics include how to use loops, conditionals, functions, arrays, and most importantly "classes". These are the building blocks of programs, which can be used to create increasingly complex programs.

Prerequisite: CSI2100-Computer Programming

This course explores the concepts of heuristics and optimization as two means of problem-solving and analysis. Topics include: dynamic programming; linear programming; constrained Boolean satisfiability; constraints programming; search. Pre-requisites: Programming; Algorithms and Data Structures; Discrete Mathematics; Artificial Intelligence. 

This course offers a study of software system design using the C programming language. Topics include: basic data types and flow constructions; structure of a C application; pointer manipulation; dynamic data structures; memory leaks; concurrent tools; tools for detecting memory leaks; Linux kernel, processes, and filesystems; main libraries; concurrency. Pre-requisites: Programming; Systems Programming. 

This course introduces the social, ethical, legal, and professional issues involved in the widespread deployment of information technology. It stimulates students to develop their own, well-argued positions on many of these issues.

Students think about the social and ethical implications of the widespread and sustainable use of IT; develop awareness of the laws and professional codes of conduct governing the IT industry; explore IT industry working practices, including the need for continuing professional development; develop information gathering skills; and adopt principled, reasoned stances on important issues in the topic area. 

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrolment is by permission of the instructor. The course consists of theoretical lessons and practical sessions. In each lesson, after a theoretical introduction, a practical session takes place in which the student is asked to experience the introduced topic first-hand. The course is organized in two modules. The first module covers basic programming concepts, the second module covers advanced topics. The topics of the lectures include:

• Introduction to programming
• Introduction to the Python language
• Importing and Exporting data and text in Python
• Manipulating data and text in Python
• Describing and visualizing data in Python
• Libraries for Machine Learning

At the end of the course, the student has competences on theoretical and practical foundations for the acquisition, manipulation, and analysis of text and data using computational tools. Furthermore, the student will be familiar with the methodological foundations for the development of scripts for natural language processing. They know and use the fundamental algorithms and data structures and are able to build and interpret graphs that show descriptive statistics of the data collected in order to facilitate its analysis.

This course provides a basic introduction to machine learning (ML) and artificial intelligence (AI). With an algorithmic approach, it offers a practical understanding of the methods that are reviewed, not least through their own implementation of several of the methods. The course covers supervised classification based on, for example, artificial neural networks (deep learning), in addition to unsupervised learning (cluster analysis), regression, optimization (evolutionary algorithms and other search methods) and reinforcement learning, as well as design of experiments and evaluation. The course also provides an introduction to philosophical fundamental problems and ethical issues related to ML/AI, in addition to the history of the field.