"Juhasz Gyula" Faculty of Education
"Juhasz Gyula" Faculty of Education / /Department of Computer Science
| Course Code | 11.30 |
| Module | 0 |
| Title: | Introduction to graph theory |
| Teacher: | Gábor Galambos |
| Contact: | galambos@jgypk.u-szeged.hu |
| Level | BA |
| Termin | 1st |
| Module Aims |
This course introduces the students to the basic knowledge of graph theory Beside the theoretical backgrounds we show some practical problems where the theoretical results can help to solve these problems. |
Module Subject |
Introduction Fundamentals and Elementary Results: In this section we introduce those of definitions which one needs to understand the most important basic connections in the graph theory The Structure of Graphs Connectivity Hamiltonian Graphs Euler Graphs Planar Graphs Electrical networks Matching Graph colouring |
| Number of Credits | 4 |
| Course Code | 11.30 |
| Module | 0 |
| Title: | Introduction to Algorithms and Data Structures 1 |
| Teacher: | András Erik Csallner |
| Contact: | csallner@jgypk.u-szeged.hu |
| Level | BA |
| Termin | 1st |
| Module Aims |
The aim of the course is to make students familiar with the basic notions, principles, notation and tools of algorithm theory, and enable them to program basic algorithms using a high level programming language. |
Module Subject |
• Structured programming and algorithm description methods: flow diagrams, pseudocode • Type algorithms and special algorithms (recurrences and backtracking algorithms) • Analysis and complexity of algorithms • The asymptotic notation • Formulating time complexity • Basic data structures, stacks, queues, linked lists, pointers and rooted trees • Binary search trees, operation over BSTs, and binary search • Sorting, insertion sort and merge sort • Heaps and priority queues; heapsort and quicksort • Greedy algorithms (Huffman codes) • Representation of graphs; elementary graph algorithms • Single-source shortest path methods |
| Number of Credits | 4 |
| Course Code | 11.30 |
| Module | 0 |
| Title: | Introduction to Data Mining and Knowledge Discovery |
| Teacher: | Miklós Krész |
| Contact: | kresz@jgypk.u-szeged.hu |
| Level | BA |
| Termin | 2nd |
| Module Aims |
Data mining is a broad area that integrates techniques from several fields including machine learning, statistics, artificial intelligence, and database systems, for the analysis of large volumes of data. This interdisciplinary course gives a wide exposition of these techniques. The course is recommended for students in Informatics, Engineering, Natural sciences or Economics. |
Module Subject |
1. Introduction to Data Mining 2. Machine Learning and Classification 3. Input: Concepts, instances, attributes 4. Output: Knowledge Representation 5. Classification: Basic methods and decision trees 6. Evaluation and Credibility 7. Data Preparation for Knowledge Discovery 8. Clustering 9. Associations 10. Visualization 11. Graph mining and social network analysis 12. Mining Object, Spatial, Multimedia, Text and Web Data 13. Applications and Trends in Data Mining 14. Data Mining Software Tools |
| Number of Credits | 4 |
"Juhasz Gyula" Faculty of Education / /Department of French language and literature
| Course Code | 09.00 |
| Module | Francais general pour etudiants erasmus |
| Title: | language study |
| Teacher: | Olivier LEMAIRE |
| Contact: | bacskai@jgypk.u-szeged.hu |
| Level | |
| Termin | 1st |
| Module Aims |
Initiation au francais général. Développement de l'expression orale et écrite. |
Module Subject |
Ce cours est une initiation au français pour étudiants erasmus débutants. On abordera les thématiques et actes de parole suivants: Se présenter, s'informer sur l'identité de l'autre, demander/donner des informations personnelles, compter, demander le prix de quelque chose, indiquer ses goûts, parler de ses passions, ses rêves, parler de sa ville, localiser, demander/donner des explications, indiquer un itinéraire simple, écrire une carte postale, donner ses impressions sur un lieu, parler de ses activités, dire quel temps il fait. |
| Number of Credits | 2 |