The study programme provides students with comprehensive knowledge and sound theoretical foundations in the following areas: software engineering methodologies, SE technologies, optimization of algorithms, integration of system components, parallel algorithms, programming on mobile platform, programming on distributed web platform, data management, data analysis and DW systems, text mining technologies. Graduates are able to carry out and coordinate research and development tasks in computer science.
Head of the program: dr. László Kovács (Ez az emailcím a szpemrobotok elleni védelem alatt áll. Megtekintéséhez engedélyeznie kell a JavaScript használatát.)
Curriculum description
Graduation requirements
The duration of the study period is 14 weeks for each semester. The Master students have to fulfill two years (four semesters) to succeed and get their Master degree in the selected profession.
All the students are required to pass exams for taken subjects during each semester. Each subject requires pre and final exams.
The time of preexams are due to professor decision during the semester, but the usual time for finishing preexam is two weeks before the end of the semester.
The students in their third semester chose the topic of their interests calling the professor attention whom they want to perform their dissertation. The precondition to work or start the dissertation is to fulfill to subjects called Degree thesis (A), and Degree thesis (B).
The students are required to perform the professional practice. The professional practice usually is the performance of some practical work by the students. The students perform the professional practice whether in the University or sometimes out of the University in a company, workshop or a factory. If the students find any internship possibility, are allowed to fulfill his/her professional practice at the selected company, too.
The students who fail in one or more subjects, can repeat the exam without charging/fining only for the first time. If the number of failed subject happens more than once, they have to pay for the exam/exams repetition. The number of the repetition is not unlimited. It means after the third exam, the dean permission and after the fourth one, the exam repetition only can be performed by the Rector permission.
Some regulation concerning the study at the University of Miskolc. Recommended to keep on the following steps:
 At the University of Miskolc, the students are expected to attend classes every day of the 12 weeks of their study period and to be on time for each class they attend.
 Students are to take at least 65% of the lessons performed by the professor.
 The professors are asked to report the student's attendance sheet each month.
 The students have to take two types of exams, theoretical and practical ones. Taking both of them is compulsory. The successful students make a study plan and divide time for all lessons which they have taken during the study period.
 If the students fail at one or more different subject, for the first time they can repeat the exam/exams without charging them. If the students fail for the second or more times, they are requested to pay for the failed exams.
 Concerning internship and professional practice, the students taking the summer classes for practice. Fulfilling professional practice lesson is required to fulfill in the University or out of the University. Depending on the University cooperation with the companies, students can get the opportunity to spend their summer practice in a factory or a company. Otherwise, they have to use the university facilities for this purpose.In case the students can find an internship, it is possible to get the possibilities to perform his practice subject at the relevant company or factory.
 The students have to pass the following requirements:
1 Diploma work of size 4070 pages approved by the supervisor
2 Preparing a hard copy of diploma work after correction,
3 Review of the diploma work by external reviewers
4 The state/complex exams and open defence of the diploma work
Course descriptions
Course Title: Architectures and Embedded Systems 
Credit: 4 
Type: lecture hours / seminar (practical) hours: : 2/2 

Requirements (exam/practical mark/signature/report, essay): exam 

Suggested semester: autumn /spring first semester 

Prerequisite course(s): 

Course Objective: The purpose of the Embedded Systems and Architectures is to present embedded system design platforms and architectures, that gives general software design knowledge, extends the software technologies knowledge, presents embedded architectures (FPGA and ARM), introduces the event and time controlled programming, presents design models and model based software design. 

Midsemester exam: midterm examination and labs problems 

Assessment methods and criteria: after midterm exam and labs examination final examination 

Course Description: Embedded system design and design parameters, system components; Input/output interfaces, analogue signal conditioning; data processing, microcontroller and microprocesszor architectures, FPGA, signal processors; Architecture comparation; Harwaresoftware codesign; communication interfaces; Software design process; Software architectures; Embedded system’s operating systems, Realtime operating systems; model based development; system debug and system test. 

Required readings, textbooks: 1. Rob Toulson and Tim Wilmshurst: Fast and Effective Embedded Systems Design: Applying the ARM mbed, Published by Newnes, ISBN: 9780080977683 2. Andrew N. Sloss, Dominic Symes, Chris Wright, ARM System Developer's Guide, Published by Elsvier, ISBN: 1558608745 3. Cuno Pfister: Getting Started with the Internet of Things: Connecting Sensors and Microcontrollers to the Cloud, Published by O'Reilly Media, ISBN10: 1449393578, ISBN13: 9781449393571 

Recommended readings, textbooks 1. Daniel W. Lewis, Fundamentals of Embedded Software with the ARM® CortexM3, Published by Prentice Hall, ISBN10: 0132916541, ISBN13: 9780132916547 2. Jonathan Valvano: Embedded Systems: RealTime Operating Systems for the ARM CortexM3, Published by CreateSpace, ISBN10: 1466468866, ISBN13: 9781466468863 

Instructor: Dr. József Vásárhelyi, associate professor, PhD 

Teaching assistant:  
Course Title: Artificial Neural Network 
Credit: 4 
Course type: lecture hours / seminar (practical) hours: : 2 hours lecture + 2 hours practice 

Requirements (exam/practical mark/signature/report, essay): Exam 

Suggested semester: third or fourth semester 

Prerequisite course(s):  

Course Objective: The term neural network was traditionally used to refer to a network or circuit of biological neurons. The modern usage of the term often refers to artificial neural networks, which are composed of artificial neurons or nodes. Thus the term may refer to either biological neural networks are made up of real biological neurons or artificial neural networks for solving artificial intelligence problems. Unlike von Neumann model computations, artificial neural networks do not separate memory and processing and operate via the flow of signals through the net connections, somewhat akin to biological networks. These artificial networks may be used for predictive modeling, adaptive control and applications where they can be trained via a dataset. 

Midsemester exam: midterm: preexam 

Assessment methods and criteria: 2x Preexam + Final Exam (written). To improve the given mark oral exam is also recommended. 

Course Description: Work on artificial neural networks, commonly referred to as „neural networks“, has been motivated right from its inception by the recognition that the brain computes in entirely different way from the conventional digital computer. The fundamentals of Artificial Network (ANN) covers mainly the structural levels of organization in the brain, models of a neuron, neural networks viewed as directed graphs, feedback, network architectures, knowledge representation, visualizing process in neural networks, artificial intelligence & neural networks and historical problems. Furthermore learning process and perceptron structure are the essential parts at study of neural network. The basic points at this study are as follows:  Structural levels of organization in the brain,  Network architecture,  Vizualization processes in neural networks,  Learning processes (errorcorrection learning, Hebbian learning, etc.),  Supervised learning,  On supervised learning,  Correlation matrix memory,  The perceptron structure,  LeastMeanSquare Algorithm. 

Recommended readings, textbooks: 1. Simon Haykin 1999. Neural Networks: A comprehensive foundation, Ed. Prentice Hall. ISBN: 0139083855 2.Kung, S. Y., 2007. Digital Neural Networks, Ed. Prentice Hall. ISBN: 0136123260 3.Simon Haykin 1999. Neural Networks. A Comprehensive Foundation. New York: Mc Millan. ISBN: 0023527817. 4.Presented Lectures


Suggested readings: 1.Brian J. Taylor 2010. Methods and Procedures for the Verification and Validation of Artificial Neural Networks. Springer Publishing Company. ISBN: 1441939350. 2.Daniel S. Levine 2013. Neural Network for Knowledge Representation and Interference. Psychology Press. ISBN: 1134771614. 3. S.M. Sapuan, Iqbal Mohammed Mujtaba (szerk.) 2010. Applications of Neural Networks. Taylor and Francis Group. ISBN: 9781420093322. 

Instructor: Dr Samad Dadvandipour, associate professor, PhD. 

Teaching asssistant: 
Course Title: Communication Theory 
Credit: 5 
Course type: lecture hours / seminar (practical) hours: Lecture/practice (3/1) 

Requirements (exam/practical mark/signature/report, essay): Exam 

Suggested semester: Second semester 

Prerequisite course(s):  

Course Objective: The basics of analog and digital communication 

Midsemester exam: Test and report of exercises 

Assessment methods and criteria: Exam 

Course Description: Representation, description and types of signals. Continuous and discrete signals. Describing signals in time domain. Statistical average, time average, autocorrelation. Fourier transformation, describing signals in frequency domain. Sampling, quantization and coding. DFT. Coding, code types, errordetecting and errorcorrecting codes. Base concepts of data transmission. Simplex, halfduplex and fullduplex connection. Analog and digital modulation techniques. The basics of digital signal processing. 

Recommended readings, textbooks: 1. Uli sorger: Communication Theory, Books on Demand GmbH, 2009. pp. 237. ISBN:9783837085211 2. , Sudakshina Kundu: Analog and Digital Communications, 2010. pp. 367. ISBN 9788131731871. 3. P Ramakrishna Rao: Signals and Systems, Tata McGraw Hill Co., 2008. pp. 559. ISBN 9780070669277. 4. Bhagawandas P. Lathi: Linear Systems and Signals, Oxord University Press Incorporated, 2009. pp. 975. ISBN 0195392566.


Suggested readings: 1. Imre Császár, János Körner: Information Theory: Coding Theorems for Discrete Memoryless Systems, Cambridge University Press, 2011. pp. 485. ISBN 9780521196819 2. Fazlollah M. Reza: An Introduction to Information Theory, General Publishing Co., 1994. pp. 493. ISBN 0486682102. 3. Gordon E. Carlson: Signal and Linear System Analysis, Allied Publishers Ltd. 1993. pp. 733. ISBN 8170232384.


Instructor: Dr. László Czap, Ph.D., associate professor 

Teaching asssistant: 
Course Title: Complexity of Algorithms 
Credit: 5 
Course type: lecture hours / seminar (practical) hours: : 3/1 

Requirements (exam/practical mark/signature/report, essay):term mark 

Suggested semester: first semester 

Prerequisite course(s):  

Course Objective: The need to be able to measure the complexity of a problem, algorithm or structure and to obtain bounds for complexity arises in computer science, mathematics, statistics, biology, medicine, social sciences and engineering. The complexity (in computer science) is measured by the quantity of computational resources (time, storage, program, communication) used up by a particular task. These notes deal with the foundations of this theory. 

Midsemester exam: classroom test 

Assessment methods and criteria: : written examination 

Course Description: Models of Computation (Finite automata, The Turing machine, The Random Access Machine, Boolean functions and Boolean circuits) Algorithmic decidability (Recursive and recursively enumerable languages, Other undecidable problems, Computability in logic, Godel’s incompleteness theorem) Computation with resource bounds (Polynomial time, Other complexity classes, General theorems on space and time complexity) Nondeterministic algorithms (Nondeterministic Turing machines, Witnesses and the complexity of nondeterministic algorithms, Examples of languages in NP, NPcompleteness, Further NPcomplete problems) Randomized algorithms (Verifying a polynomial identity, Primality testing, Randomized complexity classes) Information complexity (Information complexity, Selfdelimiting information complexity, The notion of a random sequence, Kolmogorov complexity, entropy and coding) Pseudorandom numbers (Classical methods, The notion of a pseudorandom number generator, Oneway functions, Candidates for oneway functions) Decision trees. Algebraic computations An application of complexity: cryptography (A classical problem, A simple complexitytheoretic model, Publickey cryptography, The RivestShamirAdleman code (RSA code)) 

Recommended readings, textbooks: 1. László Lovász: Complexity of Algorithms (Lecture Notes) http://www.cs.elte.hu/~kiraly/complexity.pdf 2. Thomas H. Cormen, Charles E. Leiserson, and Ronald L. Rivest. Algorithms. Mc GrawHill, New York, 1990. 3. Donald E. Knuth. The Art of Computer Programming, IIII. AddisonWesley, New York, 19691981. 

Suggested readings: 1. Alfred V. Aho, John E. Hopcroft, and Jeffrey D. Ullmann. Design and Analysis of Computer Algorithms. AddisonWesley, New York, 1974. 2. L. A. Levin. Fundamentals of computing theory. Technical report, Boston University, Boston, MA 02215, 1996. Lecture notes. 3. Harry R. Lewis and Christos H. Papadimitriou. Elements of the Theory of Computation. PrenticeHall, New York, 1981. 4. Christos H. Papadimitriou. Computational Complexity. AddisonWesley, New York, 1994. ISBN 0201530821. 5. Christos H. Papadimitriou and K. Stieglitz. Combinatorial Optimization: Algorithms and Complexity. PrenticeHall, New York, 1982. 

Instructor: Dr Attila Házy, associate professor 

Teaching asssistant: 
Course Title: Database Systems 
Credit: 5 
Course type: lecture + lab practice: 3/1 

Requirements (exam/practical mark/signature/report, essay) exam, term mark 

Suggested semester: second semester 

Prerequisite course(s):  

Course Objective: The aim of the course to learn the basic modelling methods and techniques in persistent data management. The student will practice data modelling in standard models (relational, hierarchical, LDAP, JPA) and nonstandard models (Objectrelational, deductive, ontology OWL). 

Midsemester exam: 1. Creating three data model in different models (LDAP, ORDMS, ontology) 2. Every student creates a presentation of a specific area of Data Modelling. 

Assessment methods and criteria: : The weighted average of two notes (oral exam and practice) is calculated. The weight factors are: 0.7: oral exam, 0.3: lab practices. The minimum level for note 2 is 50%. 

Course Description: Overview of data modelling, relational data structure, relational algebra; overview of the SQL standard; JDBC programming; Hierarchical data models; tree structure, LDAP architecture, LDAP model; LDAP data management (schema and security), LDAP API; Data persistency API, mapping OOP into relational model; JPA API elements; myBatis, architecture and API programming; NoSQL databases; CAP concept, JSON structure; MongoDB data model, CRUD operatons, MongoDB Java API programming; Graph datamodel, Neo4J DB architecture, Cypher language, Neo4J Java API programming 

Recommended readings, textbooks: 

1. T. Connolly, C. Begg: Database Systems, Addison Wesley, 2005 2. G. Carter: LDAP System Administrator, : O'Reilly Media, 2003 3. E. T Ray: XML, O'Reilly Media, 2003 4. S. Dietrich; S. Urban: Fundamentals of Object Databases, Morgan & Claypool Publishers, 2010


Suggested readings:


1. Oracle Big Data Handbook, Oracle Press, 2013 2. R. Colomb: Deductive Databases and Their Applications, CRC Press , 1998 

Instructor: dr. László Kovács, associate professor, PhD habil. 

Teaching asssistant: 
Course Title: Data Analysis and Data Mining 
Credit: 4 
Course type:lecture + practice: 2/2 

Requirements (exam/practical mark/signature/report, essay) exam, term mark 

Suggested semester: third semester 

Prerequisite course(s):  

Course Objective: The aim of the course to learn the standard and uptodate data analysis methods for data warehouses. The student will practice data analysis methods and data mining algorithms (OLAP methods, data warehouses, multidimensional data models, classification, clustering). 

Midsemester exam: 1. Creating three data model in different models (MD modelling in PE, MDX programing, data mining algorithms) s2. Every student creates a presentation of a specific area of Data Analysis and Datamining. 

Assessment methods and criteria: : the weighted average of two notes (oral exam and practice). The weight factors are: 0.7: oral exam, 0.3: lab practices. The minimum level for note 2 is 50%. 

Course Description: Overview of data analysis tools and levels, basic statistical tools, Bayesian network, comparison of OLAP and OTLP; decision support tools, MD data model, semantic MD models, MD algebra, Oracle PE OLAP commands, programming MD databases in PE, Architecture of MS SQLServer OLAP DW, overview of MDX language; basic MDX queries, derived sets and measures; complex MDX functions; building a data warehouse; schema integration, ETL processes, Transformation methods; M Integration server, overview of data mining, data clustering methods, SOM, data classification methods, BPNN, SVM, mining association rules, detection of outliers, dimension reduction methods, PCA, SVD. 

Recommended readings, textbooks: 1. J. Han: Data Mining: Concepts and Techniques , The Morgan Kaufmann Publ, 2011 2. E. Thomsen: Olap solutions, Building multidimensional information systems, Addison Wesley, 2002 3. R. Stackowuiak, J. Ryaman, R. Greenewald: Oracle data warehousing, Addison Wesley, 2007


Suggested readings: 

1. R. Roiger, W. Geatz: Data Mining, Addison Wesley, 2003 2. E. T Ray: XML, O'Reilly Media, 2003 3. I. Witten: Data Mining: Practical Machine Learning Tools and Techniques The Morgan Kaufmann, 2011. 

Instructor: dr. László Kovács, associate professor, PhD habil. 

Teaching asssistant: 
Course Title: Discrete Mathematics. 
Credit: 5 
Course type: lecture hours / seminar (practical) hours: : Lectures (including projected presentations) and classes (for solving exercises). 2/2 

Requirements (exam/practical mark/signature/report, essay):To qualify for final exam it is required to participate in the classwork during semester, active participation in the lectures is also recommended. Written final exam, grade in a scale of 15. 

Suggested semester: first semester 

Prerequisite course(s):  

Course Objective: The course is an introduction to discrete mathematics which is a collection of various branches of mathematics with an emphasis on finite structures. 

Midsemester exam: two written qualifying tests 

Assessment methods and criteria: grade in a scale of 15. 

Course Description: Sets and relations, partial and linear orders, equivalences, elementary combinatorics, exclusioninclusion formula, Pascal’s triangle, Fibonacci and Catalan numbers, semigroups and groups, the symmetric and alternating groups, Lagrange and Cauchy theorems for finite groups, rings and fields, number fields, the algebra of polynomials, Euclidean algorithm, irreducible factorizations of polynomials, introduction to graphtheory, trees, the greedy algorithm, planar graphs, the chromatic number, bipartite graphs, matchings, the Turán problem, basic Ramsey theory, graphs and matrices, Boolean functions, polynomial form, disjunctive and conjunctive normal forms, clones of Boolean functions, maximal clones, completeness, Post lattice. 



Recommended readings, textbooks: 1. L. Lovász, J. Pelikán, K. Vesztergombi: Discrete Mathematics: Elementary and Beyond, Springer, 20032. Stephan Foldes: Fundamental Structures of Algebra and Discrete Mathematics, Wiley, 19943. Wallis, W.D: A Beginner's Guide to Discrete Mathematics, Birkhauser. (2002) 

Suggested readings:
1. Rosen, Kennth H. : Discrete Mathematics and its Applications, McGrawHill, 5th edition, 2003 2. Goodaire, E. and Parmenter, M : Discrete Mathematics with Graph Theory, 2nd Edition; Prentice Hall, 2002 

Instructor: dr. Jenő Szigeti, professor of mathematics 

Teaching asssistant: Dr. Sándor Radeleczki, associate professor of mathematics 
Course Title: Enterprise Application Integration 
Credit: 4 
Course type: lecture hours / seminar (practical) hours: : 2 hours lecture and 2 hours practice 

Requirements (exam/practical mark/signature/report, essay):term mark 

Suggested semester: second semester 

Prerequisite course(s):  

Course Objective: Enterprise Application Integration (EAI) aims at integrating different enterprise applications. In another word, EAI is a goal for enterprise architecture. 

Midsemester exam: Written and oral Exams+ Homeworks during the semester to evaluate the student’s understanding of the subject. 

Assessment methods and criteria: result of the written examination. Written exam: 039%:1; 4054%: 2, 5569%: 3, 7084%: 4,85100%: 5 

Course Description: Enterprise Application Integration, or EAI, has existed as a technical term since the early 2000s, but the central problem that it attempts to solve is much older. In a nutshell, EAI is an approach, or more accurately, a general category of approaches, to providing interoperability between the multiple disparate systems that make up a typical enterprise infrastructure. Enterprise architectures, by their nature, tend to consist of many systems and applications, which provide the various services the company relies upon to conduct their day to day business. A single organization might use separate systems, either developed inhouse or licensed from a third party vendor, to manage their supply chain, customer relationships, employee information, and business logic. This modularization is often desirable. In theory, breaking the task of running a business into multiple smaller functionalities allows for easy implementation of the best and newest technological advancements in each area, and quick adaptation to changing business needs. However, to gain the benefits of this kind of distributed, modular system, an organization must implement technologies that deal with the problems presented by this architecture:


Recommended readings, textbooks:


1.Alexi Leon.: Enterprise Resource Planning – Publishing by Mc Graw Hill Publishing Company Limited, Copyright 2008, Alexi Leon. ISBN(13) 9780070656802. 2. William A. Ruh, Francis X. Maginnis, William J. Brown Enterprise Application Integration, 224 pages February 2002 ISBN: 9780471437864 3. D. Chappel: Enterprise Service Bus: Theory in Practice. O'Reilly Media, 2004. ISBN10: 0596006756 p. 792 4. T. Erl: ServiceOriented Architecture (SOA): Concepts, Technology, and Design, Prentice Hall Ptr, 2005. 

Suggested readings:
1. Claus Ibsen: Camel in action, Manning Publications, ISBN10: 1935182366, p. 552, 2011. 2. G. Hohpe, B. Woolf: Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions. AddisonWesley Professional, ISBN: 0321200683, 2003. 3. D. S. Linthicum: Enterprise Application Integration. Addison Wesley, ISBN: 0201615835, 1999. 

Instructor: Dr. Samad Dadvandipour, associate professor PhD 

Teaching asssistant: 
Course Title: Geometric Modeling 
Credit: 4 
Course type: lecture hours / seminar (practical) hours: : 2+2 

Requirements (exam/practical mark/signature/report, essay):final exam 

Suggested semester: second semester 

Prerequisite course(s):  

Course Objective: to acquire the basics of curve, surface and solid modeling by means of computer, that is the theoretic background of CAD systems 

Midsemester exam: a project 

Assessment methods and criteria: grading in a scale of 15 

Course Description: 

Coordinate systems, homogeneous coordinates, matrix representation of point and coordinate transformations. Description of curves, interpolating and approximating curves, spline curves. Osculating plane, arc length, curvature, torsion, Frenet frame. Definition and properties of Hermite arc, Ferguson and Overhauser splines. Parametric description and properties of Bézier curves, de Calteljau algorithm. Parametric form and properties of Bspline curves. Description of surfaces, tangent plane, normal, surfaces swept by a moving curve. Interpolating and approximating surfaces: Coons patch, Bézier and Bspline surfaces. Generation of rational Bézier and Bspline surfaces and their properties. Surface and solid modeling in CAD systems. 

Recommended readings, textbooks: 1. Farin, G.:Curves and Surface for ComputerAided Geometric Design, 5th edition MorganKaufmann, 2002 2. Hoschek, J., Lasser, D.: Fundamentals of Computer Aided Geometric Design, AK Peters, Wellesley, 1993. 3. Prautzsch, H., Boehm, W., M. Paluszny, M., Bézier and Bspline Techniques, Springer, 2002. 

Suggested readings: 1. Gallier, J.: Curves and Surfaces in Geometric Modeling, Morgan Kaufmann Publisher, San Francisco, 2000. 2. Farin, G., Hoschek, J., Kim, M.S.: Handbook of Computer Aided Geometric Design, NorthHolland, 2002. 3. L. Piegl, L., Tiller, W., The NURBS Book, SpringerVerlag, 1997. 

Instructor:Dr. Imre Juhász, professor, Ph.D., dr. habil. 

Teaching asssistant: Sándor Lajos, educator 
Course Title: Graphics Programming 
Credit: 4 
Course type:lecture + practice: 2/2 

Requirements (exam/practical mark/signature/report, essay):exam 

Suggested semester: third semester 

Prerequisite course(s): 

Course Objective: The aim of the course is to understand methods, algorythms behind real computer graphics applied in modern computer games. This integrated knowledge helps students to create graphics oriented applications and computer games. 

Midsemester exam: Every student should create a workable graphics oriented application during the semester. 

Assessment methods and criteria: The submitted graphics application is evaluated. 

Course Description: Computer graphics fundamentals; Framebuffer; Platform specific display, the pipeline model of the graphics card; Resources, memory management. Drawing states, overview of Developer Tools and Platforms, Programming graphics cards in OpenGL environment; Elements of graphics rendering in platform independent environment; Texturing basics;The general structure and design of a graphical game engine. Relationship of models and entities. 2D visualization, animation, visibility and collision detection; Font management; Image synthesis, and graphics frameworks designs questions in 3D environments; Camera handling and speed optimization. Multitexturing; Lightning and shadows; Visibility algorithms and space subdivision; Terrain mapping, Particle System. Applying the GLSL shading language. Dynamic lighting, shadows, postprocessing effects using GLSL. Alternative display technologies: ray tracing, voxelbased visualization. Extending game engine with scripting. 



Recommended readings, textbooks: 

1. Andre LaMothe, Tricks of the 3D Game Programming GurusAdvanced 3D Graphics and Rasterization, Sams publisher, 2003, 9780672318351. 2. David H. Eberly, 3D Game Engine Design: A Practical Approach to RealTime Computer Graphics (Morgan Kaufmann Series in Interactive 3D Technology) [Hardcover], CRC Press; 2 edition, 2006, 9780122290633. 3. Jason Gregory, Game Engine Architecture [Hardcover], A K Peters/CRC Press, 2009, 9781568814131.


Suggested readings:) 

1. Matt Pharr, Greg Humphreys, Physically Based Rendering, Second Edition: From Theory To Implementation [Hardcover], Morgan Kaufmann; 2 edition, 2010, 9780123750792. 2. Tomas AkenineMoller, Eric Haines, Naty Hoffman, RealTime Rendering (Third Edition),A K Peters/CRC Press, 9781568814247. 3. Wolfgang Engel, GPU PRO 3: Advanced Rendering Techniques [Hardcover], A K Peters/CRC Press, 9781439887820. 4. Patrick Cozzi, Christophe Riccio, OpenGL Insights, A K Peters/CRC Press; Har/Chrt edition, 2012, 9781439893760. 

Instructor: dr. Péter Mileff, associate professor, PhD 

Teaching asssistant: 
Course Title: Information Theory 
Credit: 5 
Course type: lecture hours / seminar (practical) hours: : 3/1 

Requirements (exam/practical mark/signature/report, essay):exam 

Suggested semester: secondsemester 

Prerequisite course(s): 

Course Objective: To master basic concepts in information theory, including source coding, and algorithms of channel capacity. To investigate important specific codes and channels. To continue to develop problemsolving skills and to apply these skills to the solving of application problems in communication theory. Be able to apply the gained knowledge to the solution of practical problems in engineering areas through evaluation and selection of appropriate statistical techniques. 

Midsemester exam: two tests (44 practical problems, minimum: 1 solved). 

Assessment methods and criteria: exam test, 8 theoretical questions (11 point), 4 practical problems (22 points), minimum level: 4+2 points. 

Course Description: 

Source coding : entropy, Idivergence, classification of codes, KraftMcMillan inequality, source coding theorem, ShannonFano coding, GilbertMoore coding, Huffman coding, Extended Huffman coding. McMillan’s theorem; Channel capacity: joint and conditional entropies, mutual information. types of discrete memoryless channels, BSC, BEC, channel capacity, ArimotoBlahut algorithms. Channel coding: Hamming weight, Hamming distance, minimum distance decoding, single parity codes, Hamming codes, repetition codes, linear block codes, cyclic codes, syndrome calculation, encoder and decoder; Continuous source, entropy, channels, minimum entropy method. 

Recommended readings, textbooks: 1. R. B. Ash. Information Theory. Interscience, New York. 2000. 2. T. M. Cover, J.A. Thomas. Elements of information theory. Wiley, New York. 1991. 3. D. Salomon. Data Compression, Spronger, 2004 

Suggested readings: 1. S. Guiasu. Information theory with applications. McGRAWHILL, New York. 1977. 2. XueBin Liang. An Algebraic, Analytic and Algorithmic Investigation on the Capacity and CapacityAchieving Input Probability Distributions of FiniteInput FiniteOutput Discrete Memoryless Channels. Department of Electrical and Computer Engineering Louisiana State University, Baton Rouge, LA 70803. 2004. 3. Claude E. Shannon, Warren Weaver: The Mathematical Theory of Communication, Bell System Technical Journal, 1947.


Instructor: Dr. Sándor Fegyverneki, associate professor, PhD 

Teaching asssistant: Dr. Agbeko Kwami, associate professor, PhD, 
Course Title: Mobile Application Development 
Credit: 4 
Course type: lecture hours / seminar (practical) hours: : 2 lecture + 2 practice 

Requirements (exam/practical mark/signature/report, essay):term mark 

Suggested semester: 4 

Prerequisite course(s):  

Course Objective: Familiarizing of application development on mobile platforms, introducing the related development technology. 

Midsemester exam:  

Assessment methods and criteria: Planning and developing a complex mobile application and presenting the product with source code. 

Course Description: 

Overview of .NET framework and XAML standard. Principles of layout management. Data binding and applications, MVVM pattern and dependency injection. Unit testing of ViewModels. EntityFramework, cloud services. Summary of Java language. Structure of Android applications, programming activities. Overview of Android services. Building android GUI interfaces. Testing Android applications. Overview of ObjectiveC language. Introduction XCode IDE. Structure of iOS applications. Memory management, ARC. MVC pattern overview. Notifications and KVO. Build settings, schemas, targets. GUI structure, storyboarding. Integrating cocoapods. Testing iOS applications. 

Recommended readings, textbooks: 1. Darcey, Lauren; Conder, Shane: Sams Teach Yourself Android Application Development in 24 Hours (2nd Edition), 2. Robert McGovern: iOS 6 Development, Developing Mobile Applications for Applit iPhone, iPad and iPod Touch 3. Gary McLean Hall: Pro WPF and Silverlight MVVM: Effective Application Development with ModelViewViewModel, Apress 

Suggested readings: 1. Stephen G. Kochan: Programming in ObjectiveC (5th Edition) 2. M. Firtman: Programming the Mobile Web, O’Reilly Media, 2010, ISBN 9780596807788 3.. Duffy, Thomas J: Programming with Mobile Applications: Android™, iOS, and Windows® Phone, SBN10: 1133628133, 2013 

Instructor: Dr. Péter Barabás, assistant lecturer 

Teaching asssistant: 
Course Title: Mobile Communications 
Credit: 4 
Course type: lecture hours / seminar (practical) hours: : Lecture/practice (2/2) 

Requirements (exam/practical mark/signature/report, essay): Exam 

Suggested semester: Third semester 

Prerequisite course(s):  

Course Objective: The goal of the course is to help the students to understand one of the fastest growing telecommunications system: the mobile communications. 

Midsemester exam: MidsemesterTest (at least 50% of the task) + accomplishment of practices 

Hallgatók értékelése, jegy meghatározás módja: Test (at least 50% of the task) + written examination 

Tantárgyleírás: 

An overview of the development of mobile communications systems. The mobile radio channel characterization (types, classification and model). Propagation characteristics for mobile radio channel (propagation and simulation model). The propagation attenuation and fading. Diversity techniques. The concept of multiple accesses (FDMA, TDMA, CDMA). Modulation and channel coding procedures. The spread spectrum modulation. Public and closed cellular radio systems: GSM (HSCSD, GPRS), TETRA, DECT, UMTS/IMT2000. Cellular GSM mobile system. Background and standardization of WCDMA. UMTS services and applications (multimedia, video phone, image, etc.). Radio access network (UTRAN) and architecture. Mobile network design. Mobile ATM, wireless data transmission (mobile IP), WAP, Ad hoc networks, WLAN networks. Mobility security issues. Call routing and mobility management. QoS in the 3G systems. 4G systems. 

Recommended readings, textbooks: 1. Mischa Schwartz.: Mobile Wireless Communications. Cambridge University Press, 2005. pp 457. ISBN 0521843472. 2. V.Jeyasri Arokiamary.: Cellular and Mobile Communications, Technical Publications, 2009. pp. 468. ISBN 9788184315851. 3. Christopher Cox.: An Introduction to LTE: LTE, LTEAdvanced, SAE and 4G Mobile Communications. ,Wiley, 2012. pp. 352. ISBN 9781119970385. 4. Jochen Schiller: Mobile Communications, Pearson Education, 2000. pp. 416. ISBN 9780321123817.


Suggested readings: 1. Stallings, W.: Wireless Communications And Networks. PrenticeHall, 2002. pp. 576. ISBN 9780131918351
2. Jerry D.G.:The Mobile Communications Handbook. SpringerVerlag GmbH, 2000. ISBN 3540648364
3. M. R. Karim, M. Sarraf.:WCDMA and Cdma2000 for 3G Mobile Networks, McGraw Hill Professional, 2002. pp. 384. ISBN 0071385134384


Instructor: Dr. Amadou Kane, CSc (Ph.D.) associate professor 

Teaching asssistant: Roland Kilik, assistant lecturer 
Course Title: Operating Systems and Networks 
Credit: 5 
Course type: lecture hours / seminar (practical) hours: : 3/1 

Requirements (exam/practical mark/signature/report, essay):exam 

Suggested semester: first semester 

Prerequisite course(s): none 

Course Objective: introduce students to mainframe computer systems and related technologies also to the basics of realtime operating systems 

Midsemester exam: 

Assessment methods and criteria: lab work and examination 

Course Description: 

Introduction to mainframe architectures and technologies (Massive Parallel Processing, hardware redundancy, RAID technologies, clustering, storage networks, managing backups), basics of embedded operation systems, realtime operating systems, details of virtualization technologies, overview of modern file system structures, and also presentation of common OS security mechanisms. Introduction to the basic concepts of Computer Networks. Theoretical and design aspects. OSI and TCP/IP network models. Medias of physical layer; Data link layer protocols; Media Access Control sublayer (802.3, 802.11); Network layer (IPv4 and IPv6), addressing schemes, devices of the network extension; Transport layer (UDP, TCP), congestion control schemes. 

Recommended readings, textbooks: 1. Tannenbaum, Woodhull: Operating Systems, Design and Implementation, PrenticeHall, 1997, 9780136006633 2. David E. Williams, Juan Garcia: Virtualization with XEN, Syngress 2009, 9781597491679 3. Hubbert Smith: Data Center Storage, 9781466507814 4. Andrew S. Tanenbaum, David J. Wetherall: Computer Networks, Prentice Hall 2010, 9780132126953


Suggested readings: 1. Barb Goldworm & Anne Skamarock: Blade Servers and Virtualization, Wiley India Pvt. Limited, 2. Karl Kopper: The Linux Enterprise Cluster, 2005, 9781593270360 3. XFS Filesystem Structure: http://xfs.org/docs/xfsdocsxmldev/XFS_Filesystem_Structure//tmp/enUS/html/index.html 4. James F. Kurose, Keith W. Ross: Computer Networking: A TopDown Approach, Pearson 2012, 9780132856201 

Instructor: Dr. habil. Szilveszter Kovács, associate professor, PhD 

Teaching asssistant:, PhD, Dr. Dávid Vincze, assistant professor, PhD 
Course Title: Operation Research and Optimization 
Credit: 5 
Course type: lecture hours / seminar (practical) hours: : 3/1 

Requirements (exam/practical mark/signature/report, essay):exam mark 

Suggested semester: first semester 

Prerequisite course(s): 

Course Objective: Introducing to the theory of linear programming and developing optimization algorithms 

Midsemester exam: two tests 

Assessment methods and criteria: written exam; 040%: fail (1), 4155%: pass (2), 5670%: satisfactory (3), 7185%: good (4), 86100%: excellent (5) 

Course Description: Optimization models. Gradient vector, Hessian matrix. Positive, negative definite matrix. Classical optimization methods. Unconstrained optimization methods. Constrained optimization methods. Linear programming. Primal and dual problem. Duality theorem. Shadow price. Graph theory, labeling technique. Critical Path Method. Maximal flow  minimal cut problem. Kőnig problems (Marriage problem and its generalization). Conveyor, assignment, transportation problem. Hungarian method. 



Recommended readings, textbooks: 1. Rao, S.S.: Optimization. Theory and Applications. Wiley Eastern Limited, 1979. 2. Foulds, L.R.: Optimization Techniques. Springer Verlag, 1981. 3. Vanderbei, R. J.: Linear Programming, Foundations and Extensions. Springer Verlag, 4th Ed., 2014


Suggested readings: 1. Bazaraa, M. S., Sherali, H. D., Shetty, C. M.: Nonlinear Programming. Theory and Algorithms. Wiley, 2000. 2. Fletcher, R.: Practical Methods of Optimization. Wiley, 2000 3. Gass, S. I.: Linear Programming: Methods and Applications. Courier Dover Publications, 2003. 

Instructor: dr. Attila Körei, associate professor, PhD 

Teaching asssistant: 
Course Title: Parallel algorithms 
Credit: 4 
Course type: lecture + practice: 2 + 2 

Requirements (exam/practical mark/signature/report, essay): exam 

Suggested semester: third semester 

Prerequisite course(s):  

Course Objective: The goal of this lecture is to introduce parallel technics and software possibilities for making effective and fast programs of well known exercises and problems too. 

Midsemester exam: two scripts 

Assessment methods and criteria: Term mark is 1 if 039% or 2 if 4054% or 3 if 5569% or 4 if 7084% or 5 if 85100% is valid in term script. 

Course Description:Parallel architectures, parallel softwares and environments. Data parallelism. Algorithms of matrices, directions. Communication of processes. Pipeline communication, methods for system of linear equations. Data partitioning. Synchronous parallelism. Relaxed methods and algorithms. Multicomputer architectures, messagepassing programs. Parallel numeric algorithms. Parallel Virtual Machine. 



1. Bruce P. Lester: The Art of Parallel Programming, 1st World Publishing, Inc.; 2nd edition, 568 pages, ISBN10: 1595408398, ISBN13: 9781595408396, 2006. 2. Al Geist, Adam Beguelin, Jack Dongarra, Weicheng Jiang, Robert Manchek, Vaidy Sunderamarga: PVM: Parallel Virtual Machine, MIT Press, 298 pages, 1994. 3. Iványi Antal: Párhuzamos algoritmusok, ELTE Eötvös Kiadó, Budapest, ISBN: 963 463 759 0, 330 oldal, 2005. 

1. Marc Snir, Steve Otto, Steven HussLederman, David Walker, Jack Dongarra: MPI: The Complete Reference, MIT Press, 350 pages, 1996. 2. George Em Karniadakis, Robert M. Kirby II: Parallel Scientific Computing in C++ and MPI: A Seamless Approach to Parallel Algorithms and Their Implementation, Cambridge University Press, ISBN 10: 0521520800/0521520800, ISBN 13: 9780521520805, 696 pages. 3. Nancy Ann Lynch: Distributed Algorithms, Morgan Kaufmann Publishers, Inc., San Francisco, 2000. 

Instructor: dr. Péter Olajos, associate professor, Ph.D. 

Teaching asssistant: 
Course Title: Physical Basis of Information Technology 
Credit: 4 
Course type: lecture hours / seminar (practical) hours: : 2/2 

Requirements (exam/practical mark/signature/report, essay):examination 

Suggested semester: first term 

Prerequisite course(s):  

Course Objective: The course is an introduction to the fundamental concepts, phenomena, models and laws of electrodynamics and modern physics, especially some basic elements of condensed matter physics. Based on these the students can understand the operation of the most important parts of the computer hardware, e.g. the CPU and the hard disk.


Midsemester exam: midterm tests 

Assessment methods and criteria: midterm tests and final exam 

Course Description: 

An overview of electrodynamics. Magnetic hysteresis, magnetic data recording. Experimental and mathematical basis of quantum physics, basic principles, calculation methods. Quantum statistics. Structure of atoms and molecules. The bases of solid state physics, band theory of solids. Semiconductors, diodes, transistors. Superconductivity. Graphene and silicene. Quantum optics and quantum electronics. Lasers and their application in information technology. Quantum computers.


Recommended readings, textbooks: 1. Halliday, Resnick, Walker: Fundamentals of Physics, John Wiley 1981., 2008., 2011. 2. D. Jiles: Introduction to Magnetism and Magnetic Material, Taylor &Francis, 1998., 3. N. DasGuptaA. DasGupta: Semiconductor Devices, Modelling and Technology, PHI Learning, 2011. 

Suggested readings:) 1. N. Gershenfeld: The Physics of Information Technology, Cambridge University Press, 2000. 2. R. Waser: Nanoelectronics and Information Technology, Wiley, 2012. 

Instructor: Dr. Endre Kovács, associate professor, PhD 

Teaching asssistant: 
Course Title: Protection of Information Systems 
Credit: 4 
Course type: lecture / practice: 2 + 2 

Requirements (exam/practical mark/signature/report, essay): exam 

Suggested semester: third semester 

Prerequisite course(s): 

Course Objective: The aim of this course to introduction to the basics of information security and risk management. To declare some definition and firewall systems. The students should be able to build safety systems and to perform security tasks. 

Midsemester exam: weekly tasks and create one presentation until the end of the semester 

Assessment methods and criteria: 

Course Description: 

Protection from physical damage, unauthorized access. Data loss; intruders; attack against security systems; advice from DEC; source of danger, risks, threats, costs; Confidentiality, integrity, availability, functionality. concept of protection, expand concept of protection; „Need to Know”; protection domain; Access Matrix and permissions; implementation of Access Matrix: Global Table, Access Control List, Capability List; Formal methods: Bell LaPadula, Biba; MAC, DAC; Firewalls; components of firewalls; Packet filtering firewall; Circuit level gateway; Application level gateway; stateless and stateful packet filtering firewall; High Availability firewalls; VPN; Deep Packet Inspection Firewall; TCSEC, ITSEC, Common Criteria; Attack methods: DoS, SYN flood, ICMP flood, OOB Nuke, sniffer, address spoofing, DDoS; steganography, cryptography; Kerckhoff; symmetric and asymmetric cryptography; problems of key share; solutions: DiffieHellmanMerkle, public key infrastructure; PGP, NTFSEFS, digital signature and the Hash; the certificates; virus search methods. 

Recommended readings, textbooks: ● Bruce Schneier: Applied Cryptography (John Wiley & Sons, 1996, ISBN 0 471 11709 9) ● Cheswick, Bellowin, Rubin: Firewalls and Internet Security (AddisonWesley, 2003, ISBN 0 201 63466 X) ● D.T. Lindsay, W.L. Price: Information Security (Elsevier, ISBN 0 444 89219 2) 

Suggested readings: ● Harold F. Tipton, Micki Krause: Information Security Management Handbook, (Auerbach, 2000, ISBN 0 8439 9829 0) ● Peter G. Neumann: Computer Related Risks (AddisonWesley, 1995, ISBN 0 201 55805 X) ● K. Dittrich, S.Rautakivi, J. Saari: Computer Security and Information Integrity (Elsevier, ISBN 0 444 88859 4) 

Instructor: György Wagner, assistant professor 

Teaching asssistant: 
Course Title: Quality Assurance for Information Technology 
Credit: 3 
Course type: lecture hours / seminar (practical) hours: : 2/1 

Requirements (exam/practical mark/signature/report, essay):term mark 

Suggested semester: second semester 

Prerequisite course(s):  

Course Objective: The subject deals the fundamentals of quality management and the information technology applications supporting it. The standards, best practices, modelling methods of the quality management for IT projects are also discussed. 

Midsemester exam: 1 presentation, 1 coding exercise, 1 written end of term test 

Assessment methods and criteria: 

Course Description:The definition of Quality, Quality Management. Factors contributing to quality. The definition of Information Science. The role of quality in the global competitive market. Computer Aided Quality Assurance and its integration. The quality aspects of software products and software development process. Software process models. Modelling methods of computer application development. Capability Maturity Model. Quality standards of software products. Software specifications. Quality improvements of software development process. Software metrics. Human resources in the software development. Coding standards. 

Recommended readings, textbooks: 1. Daniel Galin: Software Quality Assurance From theory to implementation, Pearson Education Limited, ISBN 0201 70945 7,,2004 2. Murali Chemuturi: Mastering Software Quality Assurance: Best Practices Tools and Techniques for Software Developers, J. Ross Publishing, 2010, ISBN: 1604270322. p.376 3. Stephen H. Kan:, Metrics and Models in Software Quality Engineering (2nd Edition), ISBN 9780201729153, 2002 4. Capers Jones: Applied Software Measurement: Global Analysis of Productivity and Quality, ISBN 9780071502443, 2008, 5. Lisa Crispin, Janet Gregory: Agile Testing: A Practical Guide for Testers and Agile Teams, ISBN 9780321534460, 2009 

Suggested readings: 6. DeMarco, T., Lister, T.: Peopleware. Dorset House Publishing. 1999. 2. McConnel, S.: Code Complete. Microsoft Press. 1993. 3. Maguire, S.: Writing Solid Code. Microsoft Press. 1993., ISBN 9780321934116 7. Kent Beck: Test Driven Development: By Example, AddisonWesley Longman, 2002, ISBN 9780321146533 8. Martin Fowler: Refactoring: Improving the Design of Existing Code, AddisonWesley Professional, 1999, ISBN 9780201485677, 9. James Whittaker, Jason Arbon, Jeff Carollo: How Google tests software, 2012, AddisonWesley, p.320, ISBN: 0321803027 

Instructor: dr. Olivér Hornyak, associate professor, PhD 

Teaching asssistant: 
Course Title: Software Engineering 
Credit: 5 
Course type:lecture + practice: 3/1 

Requirements (exam/practical mark/signature/report, essay):term mark, exam 

Suggested semester: first semeste r 

Prerequisite course(s):  

Course Objective: The aim of the course to understand the modern software development process. Every aspect of the development is presented: phases by different models, approaches, new tendencies regarding today’s requirements. 

Midsemester exam: 1. Creating a real software specification document, where students work in groups to represent a software development team. 2. Every student creates a presentation of a specific area of the Computer Science. 

Assessment methods and criteria: written exam: 049%:1; 5058%: 2, 5969%: 3, 7084%: 4,85100%: 

Course Description:Basic concepts of software engineering. Features of software as a product. The software development steps and life cycle models: waterfall model, Evolutionary software development, Componentbased software development, incremental (iterative) development approach. The spiral model. Process Activities. Presentation of Software requirements.Functional, nonfunctional requirements, user and system requirements, the requirements planning process. Exploration and analysis. The requirements document and feasibility study. Scenarios ethnography. Requirements Validation of Software Design. Architectural design, system build models. Modular decomposition, functioned piping, controlling types, objectoriented design. Rapid software development. Agile Software Development, Extreme Programming, verification and validation. Static and dynamic techniques. V & V design, the concept of software quality. The process and product quality. 



Recommended readings, textbooks:


1. Ion Sommerville, Software Engineering 9th edition, AddisonWesley; 9 edition, 9780137035151. 2. Steve McConnell, Code Complete: A Practical Handbook of Software Construction, Second Edition, Microsoft Press; 2nd edition, 2004, 9780735619678. 3. Robert C. Martin, Clean Code: A Handbook of Agile Software Craftsmanship, Prentice Hall; 1 edition (August 11, 2008), 9780132350884.


Suggested readings:) 

1. Claus Ibsen: Camel in action, Manning Publications, ISBN10: 1935182366, p. 552, 2011 2. G. Hohpe, B. Woolf: Enterprise Integration Patterns: Designing, Building, and Deploying Messaging Solutions. AddisonWesley Professional, ISBN: 0321200683, 2003. 3. D. S. Linthicum: Enterprise Application Integration. Addison Wesley, ISBN: 0201615835 , 1999. 

Instructor: dr. Péter Mileff, associate professor, PhD 

Teaching asssistant: 
Course Title: Introduction to Technical English 
Credit: 4 
Course type: lecture hours / seminar (practical) hours: : 2 hours lecture + 2 hours practice 

Requirements (exam/practical mark/signature/report, essay):exam 

Suggested semester: first or second semester 

Prerequisite course(s):  

Course Objective: The main aim of the subject is to provide students’ ability expanding their knowledge in R&D (Research and Development) using the English language. 

Midsemester exam: MIDterm: preexam 

Assessment methods and criteria: 2x Preexam + Final Exam. If the average result of the Preexams is excellent (five), there is no need for Final Exam. 

Course Description:The subject covers a wide range of lessons on “Classic literature in Technical Science” and “Information Science & Technology” using texts and materials taken from textbooks, newspapers, computer magazines and websites. Classic literature in Technical Science mainly focuses on the comprehensive learning of materials needed to set up students’ language skills and ability in classic engineering sciences. The lessons are based on those materials which taken from different textbooks, they include material science, solid mechanics, fluid mechanics, electric, electronic & computer science, oil industry, energy and innovative engineering sciences. The covering topics of Information Science and Technology involve principles on computer architecture, computer application, operating system, application programs, networks, communication systems, and IT (recent and future developments). The main aim of the subject is to provide students’ ability in expanding their knowledge in R&D (Research and Development) using the English language. 

Recommended readings, textbooks: 1. Jamaloddin Jalalipour 2005. English for the Students of Mechanical Engineering. The Organization for Researching and Composing University Textbooks in the Humanities (SAMT). ISBN: 9644594223. 2. Eric H. Glendinning–John Mc Ewan 2003. Oxford English for Information Technology. Oxford: Oxford University Press. ISBN: 0194573753. 3. English for the Students of Engineering – Handbook. The Organization for Researching and Composing University Textbooks in the Humanities (SAMT). Copies given by the teacher. 4. Presented Lectures.


Suggested readings:) 1. Serope Kalpakjian–Steven Schmid 2007. Manufacturing Processes for Engineering Materials. California: AddisionWesley Longman Inc. ISBN13: 9780132272711. 2. Eric H. Glendinning–John Mc Ewan 2006. Oxford English for Information Technology. Oxford: Oxford University Press. ISBN13: 9780194574921.3. Rao, P. N. 2007. Manufacturing Technology: Foundry, Forming & Welding. Tata McGrawHill Publishing Company Limited. ISBN: 0074631802. 

Instructor: Dr Samad Dadvandipour, associate professor, PhD. 

Teaching asssistant: 
Course Title: Text Mining and Analysis 
Credit: 4 
Course type: 2 lectures + 2 lab / week 

Requirements (exam/practical mark/signature/report, essay):exam 

Suggested semester: fourth semester 

Prerequisite course(s):  

Course Objective: This course aims to provide students with an understanding of principles, issues, techniques and solutions connected with text mining. Course topics include: lexical processing, automatic indexing, retrieval models, search engines, retrieval effectiveness, information extraction, document classification, summarization, document warehousing, and tools for text mining. Student project involves working with analysis tools for text mining. 

Midsemester exam: project work 

Assessment methods and criteria: project work (40%), participation (10%), final exam (50%) 

Course Description: 

This course builds on the knowledge gained in Data Mining. Namely, text mining is a special field of data mining where information needs to be retrieved, extracted and summarized from unstructured textual data (documents). For this reason techniques of natural language processing are studied first, then classic information retrieval and extraction methods are matched to text mining problems. The clustering and classification of documents are the next issues to consider, mentioning here the standards and benefits of using ontologies. Also text summarization and abstraction methods are examined. At last but not least the operation of web search engines is analyzed. During the course students get an insight into the text mining modules of some major DBMSs and they have the possibility to take part in a text mining project using the services of GATE (gate.ac.uk). 

Recommended readings, textbooks:
1. Selected papers supplied by the lecturer. 2. .M. Weiss, N. Indurkhya, T. Zhang, F. Damerau. Text Mining: Predictive Methods for Analyzing Unstructured Information. Springer, 2005. 3. Tikk Domonkos (szerk.). Szövegbányászat (Az Informatika alkalmazásai sorozat). Typotex, 2007. (weblapja: szovegbanyaszat.typotex.hu)


Suggested readings:) 1. C.D. Manning, P. Raghavan and H. Schütze. Introduction to Information Retrieval 2. Cambridge University Press, 2008. Available at: nlp.stanford.edu/IRbook/ 3. Srivastava, Ashok and Mehran Sahami (eds.). Text mining: classification, clustering and applications. ISBN: 9781420059403. 

Instructor: dr. Baksáné Varga Erika PhD, assistant professor 

Teaching asssistant: 