a) General Computer Science

Design and Analysis of Algorithms (5 cu)

Analysis techniques. Design techniques. Models of computation and lower bounds. Algorithms on sets. Graph algorithms. Approximation algorithms for NP-complete problems. Probabilistic algorithms. Parallel algorithms.

String Processing Algorithms (5 cu)

Exact string matching. Approximate string matching. Pattern matching in static strings. Text databases and hypertext. Algorithm implementation and a comparison project.

Machine Learning (5 cu)

History. Inductive learning: Learning in the blocks world, identification in the limit, version spaces. Learning classifiers: Finite automata, case-based, rules, decision trees, neural networks, genetic algorithms. PAC-learning: basics, Occam's razor, Vapnik-Chervonenkis dimension, learning by queries, PAC and noise, relation of different models. PAC and classifier learning. Inductive logic programming. Real-world applications.

Elements of Uncertain Reasoning (4 cu)

Intuitions behind Bayesian modeling. Elements of Bayesian inference. Bayesian networks and their construction from data. Minimum encoding modeling. Applications of Bayesian modeling.

Problem Solving (2 cu)

Creative problem solving in learning, teaching, and research.

Data Compression (4 cu)

Text compression. Image compression. Compression in telecommunications. An implementation project.

Advanced Computer Graphics (4 cu)

A selection of advanced topics such as tray tracing, radiosity, solid modeling, illumination and color, scientific visualization, etc. are taken as a theme of the course. Individual and group work, report writing and oral presentations by the participants.

Fundamentals of Image Processing (2 cu)

Basic principles and methods of digital image processing.

Applications of Image Processing (2 cu)

How to use digital image processing in various applications, possibilities and restrictions.

Robotics (4 cu)

Types and applications of robots. Components of a robot. Architectures. Autonomous mobile robots: Navigation and motion planning. Robot learning: Reinforcement learning, Q learning.

Computational Biology (3 cu)

Molecular biology. Sequence comparison and database search. Fragment assembly of DNA. Physical mapping of DNA. Phylogenetic trees. Genome rearrangements. Molecular structure prediction.