###
Ph.D. Thesis (COMP600)

**Credit:** 0 |

**Lecture Hour (hrs/week):** 0 |

**Lab (hrs/week):** 0 |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

Ph. D. thesis in computer science. Presentation is required for the completion of the thesis.

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Seminar (COMP698)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 3 |

**Lab (hrs/week):** 0 |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

Seminar about the research field in Ph. D. study.

###
Ph.D. Qualifying Exam (COMP699)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 3 |

**Lab (hrs/week):** 0 |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

Qualifying exam on compulsory topics and on minor subjects.

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Combinatorial Optimization (COMP506)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 0 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:**

**
**

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Theory of Algorithms (COMP512)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 0 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

Automata, languages, complexity, computability. Finite automata, 2-head finite automata, pushdown automata, Turing machine. Chomsky hierarchy. Time complexity: class P, NP, NP completeness, NP complete problems.Space complexity: Savitch theorem, class PSPACE. Hierarchy theorems and circuit complexity

###
Selected Topics in Computer Programming (COMP521)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 3 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Selected Topics in Computer Programming - II (COMP522)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 3 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Introduction to Geographic Information Systems (COMP525)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 0 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Selected Topics in Computer Science - I (COMP531)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 3 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Selected Topics in Computer Science - II (COMP532)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 3 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Computational Statistict in Bioinformatics (COMP533)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 3 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Digital Geometry (COMP535)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 0 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Introduction to Quantum Computing (COMP542)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 3 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Classical and Quantum Information Theory (COMP543)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 0 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Petri Nets (COMP544)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 3 |

**Lab (hrs/week):** - |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

It is the purpose of this course to provide a coherent description of the theoretical and practical aspects of Petri Nets by showing how Petri Nets have been developed – from being a promising theoretical model to being a full-fledged language for the design, specification, simulation, validation and implementation of large discrete event systems.

###
Combinatorics (COMP551)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 3 |

**Lab (hrs/week):** - |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

bsets, partitions, permutations. Recurrence relations and generating functions. Stirling numbers. Latin squares. Extremal set theory. Steiner triple systems. Finite geometry. Posets, lattices and matroids. Designs. Error-correcting codes.

###
Graph Theory (COMP552)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 3 |

**Lab (hrs/week):** - |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

aphs, trees. Bipartite graphs. 0-1 matrices. Coloring. Algebraic methods of graph theory. Planarity, duality, embeddings. Hypergraphs. Graph algorithms: minimum spanning trees, graph-search algorithms: backtracking, breadth-first, depth-first search. Dijkstra algorithm.

###
Statistical Applications With Modern Techniques (COMP555)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 0 |

**Lab (hrs/week):** - |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

Regression analysis is a widely used suite of analytical techniques particularly suited to natural resources data. One of the strengths of regression is the conceptual simplicity of using an equation to represent a relationship between predictor variables and their associated response. This is also a weakness: drum into your mind the phrase “correlation does not imply causation” and you'll overcome (part of) this weakness. The goal in this course is to give you some experience with basic regression techniques that you can apply in your research, expose you to situations where regression analysis is useful (and perhaps not useful), and most of all give you enough understanding that you can evaluate regression in papers your read. The course is going to focus on underlying theory and also use of some selected software packages to for regression. However, also you need to know enough about how regression works to be able to evaluate a regression solution in a particular research situation.

###
Parallel Processing (COMP558)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 0 |

**Lab (hrs/week):** - |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

Introduction to parallel computers. Taxonomy of parallel computers. Array of processors, pipelining, multiprocessing. Systolic arrays. Complexity and efficiency of parallel algorithms. Principles of optimal parallel algorithm design.

###
Theory of Computing (COMP559)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 0 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Theory of Regression Analysis and Applications (COMP569)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 0 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

Regression analysis is a widely used suite of analytical techniques particularly suited to natural resources data. One of the strengths of regression is the conceptual simplicity of using an equation to represent a relationship between predictor variables and their associated response. This is also a weakness: drum into your mind the phrase “correlation does not imply causation” and you'll overcome (part of) this weakness. The goal in this course is to give you some experience with basic regression techniques that you can apply in your research, expose you to situations where regression analysis is useful (and perhaps not useful), and most of all give you enough understanding that you can evaluate regression in papers your read. The course is going to focus on underlying theory and also use of some selected software packages to for regression. However, also you need to know enough about how regression works to be able to evaluate a regression solution in a particular research situation.

###
Database Management Systems (COMP574)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 3 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Theory of Computing (COMP579)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 3 |
**Lab (hrs/week):** - |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Decision Making (COMP585)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 3 |

**Lab (hrs/week):** - |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

Introduction to decision making. Decision making process. Decision Trees. Decision making under uncertainty. Utility theory. Decision making under conflict. Risk theory. Decision making under risk. Prospect theory. Group decision making. Paired comparison analysis in decision making. Queuing theory. Linear regression model in decision making. Time series model in decision making. Neural network based decision making. Markov model in decision making process. Monte Carlo analysis in decision making.

###
Cryptography and Data Security (COMP586)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 3 |

**Lab (hrs/week):** 0 |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

Encrypt and decrypt messages using classical and modern conventional ciphers including block ciphers as well as public cryptographic ciphers, sign and verify messages using well known signature generation and verification algorithms. 2. Analyze existing authentication and key agreement protocols, identify the weaknesses of these protocols. 3. Download and install an e-mail and file security software, PGP, and efficiently use the code to encrypt and sign messages.

###
Artificial Intelligence (COMP587)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 3 |

**Lab (hrs/week):** 0 |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

Introduction to Artificial Intelligence (AI). AI history. Search strategies. State space search. Heuristic search. Control strategies. Knowledge representation. Rule-based representation, semantic networks, frame knowledge representation. Logical reasoning. Knowledge base and inference. Bayesian probability. Planning. Inference in first order logic. Introduction to learning. Decision trees. Game theory. Expert systems (ES). Building of ES. Main properties of ES. Prolog programming language in AI. Constraint satisfaction problems. Pattern recognition. Robotics. Distributed AI

###
Computational Intelligence (COMP588)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 3 |
**Lab (hrs/week):** 0 |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -

###
Data Mining (COMP589)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 3 |

**Lab (hrs/week):** 0 |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

Data types, data pre-processing, measures of similarity, classification, classifier evaluation and comparison techniques, basic concepts in association, clustering analysis, cluster evaluation, anomaly detection, issues in multimedia mining and text mining.

###
Mathematical&Computational Modeling of Biological Systems (COMP645)

**Credit:** 3 |

**Lecture Hour (hrs/week):** 3 |

**Lab (hrs/week):** 0 |

**Tutorial (hrs/week): ** 0 |

**ECTS:** -

Introduction for developing, analyzing and interpreting mathematical and computational models in systems biology. Throughout this course,our general goal is to use ODE and Petri nets as a tool for gaining a deeper understanding of biological systems and their dynamics. This course covers both qualitative and quantitative models and introduces corresponding modeling techniques demonstrated for various biological networks in Snoopy, CPNTools and matlab frameworks wherever and whenever it is convenient. It is assumed that the students have working knowledge in basic mathematical and computer science concepts. Otherwise the course is self-contained.

###
Network Science (COMP646)

**Credit:** 3 |
**Lecture Hour (hrs/week):** 3 |
**Lab (hrs/week):** 0 |
**Tutorial (hrs/week): ** 0 |
**ECTS:** -