Computer Science

Database systems are the most widely used software systems in any area of life related to mainly information technology, management, business as well as web applications and services. This course first introduces the fundamental concepts of databases and their design. Then, it introduces database operations like updating and searching in database systems, as well as the newest database types and systems. 

The course covers the following topics: 

• Introduction to databases and their types 

• Introducing the modeling of data and introducing database management systems 

• Introducing the relational model 

• Understanding the basics in database design 

• Learning the steps of normalization 

• Advanced normalization 

• Understanding relational algebra  

• Introducing the Standard Query Language (SQL) 

• Using SQL 

• Advanced database operations: transactions, triggers etc. 

• Using databases in Web applications or in Web services 

• Object-oriented databases 

• Introducing new database solutions and new systems for handling BigData 

• Understanding the concepts and usage of several NOSQL type database systems 

• NOSQL type Database systems 

• How to use these database systems for storing, searching and analyzing BigData 

• Programming with NOSQL databases to create new applications from web-services to data mining and handling BigData 

This course examines advanced algorithm design and analysis including linear programming, complexity and NP-completeness, and advanced algorithmic techniques.

This course covers basic concepts of robotics while exposing students to state-of-the-art robots. The course also discusses the basic theory for robotic manipulator operation and provides opportunities to design robots through two class projects. 

In this course, students study key aspects of the wider context in which their practice as Informatics professionals will occur.  Students develop individual capabilities that complement the technical capacities developed elsewhere in Informatics programs.  These include communication, reflection, reasoning, and analysis skills that consider the broader ethical and social implications of their work. The course is structured around professional and ethical behavior, and the wider context in which technologies are developed and deployed. 

This course examines how to better understand data, present clear evidence of the findings to the intended audience, and tell engaging data stories that clearly depict the points made though data graphics. 

This course offers a study of advanced Big Data analytics. Topics include: e-business and market trends; supply chain management; enterprise resource planning and customer relationship management; applications of advanced Big Data analytics.

This is an individual study project. Students must have a well thought-through idea of the theme of the study. A faculty teacher is appointed as supervisor, and an agreement is signed between the student and the teacher describing the title, contents, and ECTS credits of the study. A supervisor normally meets with the student between two and four times to discuss the progress of the individual study, or any problems encountered. Most supervisors also choose to read and comment on parts of the study. Students applying to do an individual study must submit a detailed project description with their application. Exams for Individual Study Projects may be oral, written or a combination of the two. This version of the course is worth 12 quarter units and corresponds to a workload of 412 hours. 

This course introduces classic and state-of-the-art methodology in computer graphics. We will focus on methods and best practices in geometry and physical simulation, which are the basic building blocks for downstream applications such as animation, industrial design, game engineering, structural analysis, AR/VR, and medical imaging. Our curriculum will cover basic representations of shapes, geometric optimization, analysis, and principles of robust digital simulation of physical scenes. The techniques employed will involve classical numerical analysis up to deep geometric learning.

The course will include programming tasks to implement a few key algorithms in geometry processing, geometric learning, and physical simulation, to the extent that they can independently run and be analysed on modest open-source data.

This course (CGGS) and Computer Graphics: Rendering (CGR) are both courses that require no previous knowledge of computer graphics. These two courses may be taken independently or together. CGGS focusses on the representation, processing, and dynamics of 3D objects in the virtual world while CGR focusses on the rendering of virtual worlds as photo-realistic images.

This course examines advanced topics and techniques in database systems, with a focus on the system and algorithmic aspects. It will also survey the recent development and progress in selected areas. Topics include: query optimization, spatial-spatiotemporal data management, multimedia and time-series data management, information retrieval and XML, data mining.

This course focuses on how artificial intelligence (AI) can be used to understand human behavior and how psychological phenomena in people can be found in artificial intelligence. Central areas are the borderland between AI and psychology include AI and ethics; Abstract, biological, and deep neural networks; AI to analyze behavior and brain data; Psychological phenomena in artificial cognition; Natural Language Processing; and Simulating behavior using AI. The course emphasis understanding of how natural cognition can be understood with the help of artificial cognition and how psychological phenomena arise in artificial cognition. Application is made in neural networks, natural language processing, AI-generated images and how AI affects society. The course contains theoretical lectures, laboratory work, and a project work.