Faculty of Science and Informatics
Faculty of Science and Informatics / /Institute of Informatics
| Course Code | 11.30 |
| Module | 0 |
| Title: | Algorithms and Data Structure 1 |
| Teacher: | Csanád Imreh |
| Contact: | |
| Level | |
| Termin | 1st |
| Module Aims |
The students have to learn the basic datastructures (linked lists, binary heap, stack, queue, trees) some advanced datastructures (binary search trees, priority queue) and some basic technologies to design algorithms (divide-and-conquer, programming greedy, dynamic programming). Moreover, they have to learn the most important sorting algorithms, and graph algorithms. |
Module Subject |
Overview of algorithmic design, Asymptotic notations and their properties Time complexity analysis of algorithms Elementary data structures (linked lists, binary heap, stack, queue, trees), advanced data structures: binary search trees, priority queue. Algorithm design techniques (divide-and-conquer, greedy, dynamics programming); Sorting and searching algorithms. Graph algorithms, Breadth-First-Search (BFS) and shortest paths, Depth-First-Search (DFS), Topological sorting, Strongly connected components, Minimum Spanning Trees (greedy algorithms: Kruskal, Prim), Single Source Shortest Paths (Dijkstra), All Pairs Shortest Paths (dynamic programming: Floyd-Warshall) |
| Number of Credits | 4 |
| Course Code | 11.30 |
| Module | 0 |
| Title: | Algorithms and Data Structure 2 |
| Teacher: | Csanád Imreh |
| Contact: | |
| Level | BA |
| Termin | 2nd |
| Module Aims |
The students have to learn many advanced data sructures (Hash tables, AVL trees.Red-black trees, B-trees, Disjoint sets management, Fibonacci heap, binomial heap), and some advanced techniques of designing algorithm (backtrack, branch and bound). Moreover they have to learn some important subfield of the theory of algorithms. |
Module Subject |
Medians, sound samples, the k-th smallest element selection algorithms. Backtracking (n Queen, knapsack) Branch and bound (knapsack). Advanced data structures (Hash tables, AVL trees, Red-black trees, B-trees, Disjoint sets management, Fibonacci heap, binomial heap) Amortized analysis. Special areas of algorithmic design: geometric algorithms, pattern matching (the Knuth-Morris-Pratt algorithm), arithmetic algorithms (public key encryption), approximation algorithms (travelling salesman), online algorithms, randomized algorithms. |
| Number of Credits | 4 |
| Course Code | 11.30 |
| Module | 0 |
| Title: | Databases |
| Teacher: | Péter Balázs |
| Contact: | |
| Level | BA+MA |
| Termin | Both |
| Module Aims |
The course is to provide mathematical foundations and practical techniques for the design, implementation, and manipulation of databases; databse modelling, normalization, relalional algebra, SQL language, transaction management, privileges. Education Aims: To introduce the fundamentals of designing and implementing databases. To gain practical experience in desgning and writing programs for manipulating databses. |
Module Subject |
The Entity-Relationship model The relational data model From E-R diagrams to relational models Relational algebra The redundancy problem: functional dependencies, decomposition, normal forms SQL language: relational schema definitions, queries, subqueries, views Constraints and triggers in SQL Embedded SQL: shared variables, cursors Transaction management, privileges Application work: designing and implementing a database application |
| Number of Credits | 5 |
| Course Code | 11.30 |
| Module | 0 |
| Title: | Digital image processing |
| Teacher: | Kálmán Palágyi |
| Contact: | |
| Level | BA+MA |
| Termin | Both |
| Module Aims |
The course is to provide mathematical foundations and practical techniques for digital manipulation of images; image acquisition; preprocessing (smoothing, filtering, restoration); segmentation; feature extraction; shape representation; and compression. Education Aims: To introduce the fundamentals of digital image processing theory and practice. To gain practical experience in writing programs for manipulating digital images. |
Module Subject |
1. Image sampling and quantizing 2. Fourier transform 3. Convolution 4. Point operations and histogram transformations 5. Image enhancement in spatial domain 6. Image enhancement in frequency domain 7. Feature (line, edge, corner) detection 8. Image segmentation 9. Shape representation and description 10. Image coding and compression |
| Number of Credits | 5 |
| Course Code | 11.30 |
| Module | 0 |
| Title: | Operations Research |
| Teacher: | András Pluhár |
| Contact: | |
| Level | All |
| Termin | Both |
| Module Aims |
The course is to provide mathematical foundations and examples of Operations Research. It introduces the basic ideas of Linear Programming, how to build models, standard forms, the main issues of simplex algorithm and the two-phase simplex method. The LP duality and its use is also covered, such as the geometric ideas behind the algrebraic machinery. |
Module Subject |
1. The basic model of Linear Programming 2. Standardization, Diet problem 3. Simplex algorithm, iteration, termination 4. Cyclization, Bland's theorem 5. Two-phase Simplex method, initialization 6. The speed of different methods 7. Fundamental theorem of LP, all optimal solutions 8. Duality and complementarity theorems, and their use 9. The economic significance of Strong duality 10. Geometry of LP, matrix games, Minimax theorem |
| Number of Credits | 5 |
Faculty of Science / Physical Geography and Geoinformatics