Computer Science

This course teaches how to identify opportunities for innovation and develop user-centered, impactful, and innovative digital solutions that respond to real-world needs. Through a combination of theoretical insights and practical tasks, explore how new digital solutions can drive change across various industries and societal needs.

Work in teams on real-world problems, realize bold ideas, and develop MVPs (minimum viable products) with mentoring and supervision. Key skills include market analysis, requirement elicitation, innovation strategy, solution making, and effective pitching.

This course uses a human-centered lens to examine security and privacy, focusing on how design and research can create solutions that people can understand, trust, and use.  

Security and privacy are as much about people as they are about technology. Many failures arise not just from a lack of technical capability, but from mismatches with how people think, behave, and interact in their everyday contexts. 

Students engage with real-world topics ranging from authentication and security warnings to deceptive patterns, AI privacy, and privacy and security challenges in sensing environments, while learning foundational methods in user research and usable security and privacy evaluation. Through critical readings, class discussions, and hands-on projects, students develop skills to understand and design for human factors in security and privacy contexts. 

Key themes include: 1) Human-centered research methods for security and privacy, 2) Usable security tools, access control, and warnings 3) AI-enabled security and privacy challenges, 4) Sensing environments and security/privacy issues, and 5) Ethics and social implications in security and privacy 

This course provides a broad overview of the field of bioinformatics, with a focus on practical application and interpretation of results from tools used in everyday biological research. Assumed Knowledge in MAT15403 Statistics 2.

This course covers homology, cohomology and applications, CW-complexes, and basic notions of homotopy theory.

There are two distinct parts to this course. The first few lectures provide students with a general overview of connectionism: its origins as an attempt to model the functioning of the brain, and the various classes of algorithms created starting from these foundations. The second part focuses on the last 10-15 years. The course provides a general framework for designing machine learning models that deal with complex structured data, introduces graphical models and Bayesian networks, and describes inference and learning algorithms for them. The course also addresses the case of neural networks, i.e. to describe possible strategies for effectively training them in real-world scenarios.

This course covers important algorithms and theories for data mining. Data mining refers to theories and techniques for finding useful patterns from massive amounts of data. Data mining has been used in high impact applications including web analysis, recommendation system, fraud detection, cyber security, etc. 

Main topics include finding similar items, mining frequent patterns, link analysis, link prediction, recommendation system, data stream mining, clustering, graph mining, time series prediction, and outlier detection. 

Prerequisite: Students should have an undergraduate-level knowledge on the following topics: Algorithms, Basic probability, Programming, Linear Algebra 

The course will provide some background but will be fast paced. 

Nowadays Cloud Computing is everywhere. Cloud Computing (CC) is not a revolution of Information technology (IT), but It is one of the key evolution steps of IT. It is computing as a utility, which has recently emerged as a commercial reality. The main characteristics of CC are 1) the illusion of infinite computing resources, 2) the ability to pay-as-you-go, and 3) the elimination of an up-front commitment by Cloud users. In other words, CC is a style of computing which can be scaled dynamically, and virtualized resources are provided as a service over the Network. The key idea behind this course is to provide fundamental CC topics taking into account both technology and business considerations. The course is divided into a series of lectures, each of which is accompanied by one or more hands-on exercises. Some of the topics covered are: Fundamental CC terminology and concepts; CC definition an its specific characteristics; Benefits, Challenges and Risks of CC platforms and Services; Roles of CC administrator and owners; SaaS, PaaS, and IaaS delivery models and their combinations; Various Public, Private, and hybrid CC environments; Business Cost models and Service Level Agreements for CC; Case Studies: Google Cloud, Microsoft Cloud, and Amazon Cloud.

Because of the development of music-related AI area and multidisciplinary trends in science and music, the skills of digital music and audio synthesis are gradually needed by industries. The knowledge of digital music involves three areas: music, electrical engineering, and computer science. This course teaches how to program and design digital music, utilizing related programming languages, including chucK (for sound synthesis), Python (for edit and analyzing MIDI data), and Scratch (for auditory-visual interactive projects). 

Course Prerequisite: "Learning Programming for Music" or any other related text-based programming courses. 

This course provides general knowledge in radio frequency applications, especially those which are common in radio communications. The fundamentals are introduced without penetrating the electronics or design details. The different parts are treated as functional blocks defined by their physical properties. This gives a basic understanding of the radio receiver or the cellular phone but also the requirements put on the used circuits. Thus, this is a compulsory course for those who later want to specialize as radio frequency designers.

This course covers various proof techniques and provides practice proving sample propositions using these techniques. Students learn basic discrete mathematics and theoretical computer science topics such as sets and functions, and practice proving propositions related to these topics. The course also covers intermediate discrete mathematics topics, including trees and graphs, and provides practice proving related propositions. Students also learn additional discrete mathematics topics (e.g., counting, probability), and apply proof techniques to prove related propositions. While there is no specific prerequisite course required, students should have basic mathematical knowledge.