Computer Science

This course covers the theories of modern deep learning and provides a practical opportunity to implement necessary deep neural network modules.

This course provides an introduction to human-computer interaction, specifically quantitative approaches to human-computer interaction research. It looks at what problems may arise in the process and how to solve those problems. It also explores how user studies are designed, conducted, analyzed, and reported.

This course introduces the design and implementation of fundamental data structures and algorithms. Topics include basic data structures (linked lists, stacks, queues, hash tables, binary heaps, trees, and graphs), searching and sorting algorithms, basic analysis of algorithms, and basic object-oriented programming concepts. The course requires students to take prerequisites.

In this course, we build on foundation from database systems, focusing on two important issues. The first issue concerns how to deal with large volumes of data that do not have the precise record structure found in databases. The amount of unstructured data (primarily text) in the world far exceeds the amount of structured data. Searching through text requires a very different approach, especially because the number of results can be extremely large, making ranking based on relevance essential. This field is known as Information Retrieval (IR). Although this discipline has existed for quite some time, its relevance has increased in recent years due to the demand for web search engines. Become familiar with basic IR concepts such as precision, recall, Boolean search, indexing and posting lists, term weighting, the vector space model, and relevance feedback. Also take a detailed look at Google’s PageRank algorithm. This part includes a practical assignment in which IR techniques are applied to processing queries on relational databases, addressing the problem that the number of results can be either too large or too small. the second issue is how to extract interesting patterns and models from data. This is the domain of data mining and machine learning. Here too, the emphasis is on the analysis of unstructured data (again, primarily text), such as using data mining for document classification and clustering, as well as for ranking documents based on their relevance to a given query. The term “document” should be interpreted broadly: it may refer to web pages, email messages (spam or not spam?), posts to a newsgroup, or even tweets. The techniques covered include, among others, Naive Bayes classification, nearest neighbor, support vector machines, hierarchical clustering, and partitioning methods such as k-means clustering. This part also includes a practical assignment in which the data analysis techniques discussed in the lectures be applied to problems as described above. For this, we use the data analysis system R. Assumed previous knowledge in Databases (INFODB), Graphics (INFOGR), and Research Methods in Computer Science or Game Technology. If you have not passed these courses (or other courses in which you acquired comparable prior knowledge), we advise you not to choose this course.

This course provides a study on file structures and database management. It examines logical database design, from the relational model to the basic physical level in order to understand and recognize the need for secondary storage media in various use cases. The course also covers the following topics: static and dynamic properties of the relational model; base structures; auxiliary structures; database management systems; storage paradigms.

This course is part of the Laurea Magistrale degree program and is intended for advanced level students. Enrollment is by consent of the instructor. The course content is divided into three distinct parts. The first part of the course discusses the evolution of the discipline from Human Computer Interaction to User Experience Design, focusing on the human, the computer, and their interaction. The second part of the course is on usability analysis and design, topics include a systematic discussion of the techniques and standards for the management of the process of user experience design, with particular attention to the phases of usability analysis (with and without the participation of users), and the user- and goal-oriented usability design methodologies. The third part of the course examines the guidelines, patterns, and methods for usability design. During this section the course discusses, with historical aspects, the framework on which the concrete aspects of usability design is based, and strong attention is given to the problem of usability for web applications and mobile apps.

This course explores the mechanisms, implications, and ethics of an environment where artificial intelligence plays an increasingly important role. Students consider the science behind the headlines to help students develop an informed opinion regarding the complexities of the use of AI in society. Students also examine the conceptual frameworks behind AI methodologies and the sources of the data on which they operate. This course provides an introduction to computational thinking on what sort of problems AI can realistically be expected to help with. Students analyze a series of case studies highlighting the use of AI in work and society.

This course in information science involves an individual course of study and is usually carried out under the supervision of an academic advisor. Students conduct and assist in research done in the Human-Computer Interaction Lab in the Information Science department. The course focuses on researching eye trackers and designing user studies using the eye trackers and collaborating with other lab projects.