MSc Thesis
The preparation and defence of a thesis demonstrating the candidate's ability for independent and original research.
Coding Theory
The main concepts, problems and applications related to error-correcting codes. Different classes of codes and their properties. Emphasis on algorithms relating to codes, examination of algorithms for encoding and decoding, together with algorithms that may be used in computer searches for specific classes of codes.
Logic in Computer Science
A thorough introduction to mathematical logic, covering the following topics: propositional and first-order logic; soundness, completeness, and compactness of first-order logic; first-order theories; undecidability and Gödel's incompleteness theorem; and an introduction to other logics such as intuitionistic and modal logics. Furthermore, the course stresses the application of logic to various areas of computer science such as computability, programming languages, program specification and verification.
Universal Algebra for Computer Science
The study of the concepts and constructs of Universal Algebra, such as products, subalgebras, homomorphic images and congruences, term algebras, free algebras, its connections with Logic and Model Theory, decidability issues, lattices and relation algebras, and applications in Computer Science such as Type Theory, Specification, Complexity Theory, Uncertainty Management and others.
Parallel Algorithms
Introduction to parallel processing, various parallel computational models including both shared-memory and distributed-memory models, speed-up, cost, design and analysis of parallel algorithms and data structures for a variety of problems in searching, sorting, graph theory, computational geometry, strings, and numerical computation; brief introduction to parallel complexity.
Introduction to Lambda Calculus
Introduction to typed and untyped lambda calculi and their semantics. Syntax of the lambda calculus, conversion, fixed points, reduction, Church-Rosser theorem, representation of recursive functions, lambda models. Category theory, cartesian closed categories and categorical models of lambda calculus.
Genetic Programming
The synthesis of computer programs using evolutionary computation. The study of different representations, including tree, linear, grammatical. Theoretical analyses, including the effects of operators, representations, fitness landscapes. Practical applications in problem solving, decision making, classification, computer vision, design.
Robot Control Architectures
Survey of approaches to control in single and multi-robot systems. Examination and study of different mobile robot control architectures, including deliberative, reactive and hybrid with focus on the issues of resolving the fundamental conflict between thinking and acting, i.e., high-level deliberation and real-time control. Other relevant topics including communication techniques in multirobot systems and safety characteristics of the studied control architectures.
Computer Vision and Visual Computer Learning
Introduction to computer vision and pattern classification. The problems of WHAT and WHERE. The issue of knowledge representation and performance. Knowledge consolidation models. The concept of recursive (i.e. evolutionary) computer learning. Visual learning. Guided learning from infallible and fallible experts. Autonomous learning and experimentation. Analysis of HPC architectures conducive to visual computer learning.
Evolutionary Computation
Study of basic concepts of evolutionary algorithms (EAs) from a theoretical and application viewpoint. This includes genetic algorithms, evolutionary strategies, genetic programming, problem representation, genetic operations, overall control, theory of EAs and various examples of important applications. Includes related bio-inspired sub-areas such as swarm intelligence, and evolutionary robotics.
Directed Reading
A reading course designed for the individual student and subject to final approval by the department graduate committee. Usually offered by the student's thesis supervisor but may also be offered by other faculty members after consultation with the supervisor.
Non-invasive Data Analysis
A study of data analysis using information from the given data based on the rough set data model. Includes an overview of the data modeling process, principles of probability, non-parametric significance testing, data filtering and discretization, model selection, rule validation, case studies.
Selected Topics in Computer Science
Various advanced topics in computer science offered by faculty members.