Computer Science

This course teaches about bio-inspired algorithms for optimization and search problems. The algorithms are based on simulated evolution (including Genetic algorithms and Genetic programming), particle swarm optimization, ant colony optimization as well as systems made of membranes or biochemical reactions among molecules. These techniques are useful for searching very large spaces. For example, they can be used to search large parameter spaces in engineering design and spaces of possible schedules in scheduling. However, they can also be used to search for rules and rule sets, for data mining, for good feed-forward, or recurrent neural nets and so on. The idea of evolving, rather than designing, algorithms and controllers is especially appealing in AI. In a similar way it is tempting to use the intrinsic dynamics of real systems consisting e.g. of quadrillions of molecules to perform computations for us. The course includes technical discussions about the applicability and a number of practical applications of the algorithms.

This course teaches the principles of software development for medium to large software design and implementation. Students apply these principles to software systems in practice by working on group projects. Through this experience, students learn how to build correct and high-performance software.

Health Data Science is an area that combines scientific inquiry, statistical knowledge, substantive expertise, and computer programming in the area of healthcare and biomedicine. Students are introduced to fundamental data analytic tools and techniques, and learn how to use specialized software to analyze real-world health data.