Subject
Intelligent Systems
| Subject | Intelligent Systems |
| Subject code | COM534 |
| Contact | Andrea Kő |
| Department | Department of Information Systems |
| Level | G |
| Lectures | 2 |
| Seminars | 2 |
| Credit | |
| Prerequisities | There are no special requirements |
| Office hours | Tuesday 11.30-12.30 |
| Classes | Tuesday 13.40-15.10. 15.30-17.00 |
| Description | Today’s networked computer systems enable executives to use information in radically new ways, to make dramatically more effective decisions -- and make those decisions more rapidly. Intelligent systems course offers a comprehensive overview about management support system technologies, and how they can be used for better decision making. Course go far beyond traditional approaches, it is focusing far more coverage on web-enabled tools, business analytics, intelligent systems over the Internet (semantic web) and other recent innovations. Each significant new technology will be introduced, and working mechanism is demonstrated. In many cases practical guidance on integrating it into real-world organizations is showed. Examples, products, services, and exercises are presented throughout. Topics include: decision support technology, OLAP, business performance management, data, web and text mining, expert system and knowledge management technologies. Course will also cover the following areas: data warehousing, including access, analysis, visualization, modeling, and support. Real business scenarios for the use of advanced management support technology are presented. The course is supported by a web site containing additional readings, relevant links, and other supplements. |
| Program | Detailed class schedule: Date of class Topics to be discussed, readings required for the class Week 1 Foundation of intelligent systems course Topics covered during the course, students’ expectations, course requirements, and individual presentations How to use CooSpace System Reading Chapter 1. Week 2 Decision making systems, modeling Reading Chapter 2. Week 3 Decision support systems Reading Chapter 3. Week 4 Modeling and analysis Reading Chapter 4. Week 5 Data acquisition, data warehousing Reading Chapter 8. Week 6 Business analytics, OLAP, Business Performance Management Reading Chapter 9. Tableau lab work Week 7 Tableau lab work Midterm exam Week 8 Tableau assignment Week 9 Data mining Reading Chapter 5. Rapidminer lab work Week 10 Text and web mining Reading Chapter 7. Rapidminer lab work Week 11 Rapidminer assignment Week 12 Knowledge management Reading Chapter 11 Week 13 Artificial intelligence and expert systems Reading Chapter 12. Week 14 Advanced intelligent systems Reading Chapter 12 Week 15 Final exam Week 16 Make-up exam |
| Course materials | Compulsory reading: Efraim Turban, Ramesh Sharda, Dursun Delen: Decision Support and Business Intelligence Systems, 9th Edition Prentice Hall, 2010, ISBN: ISBN-10: 0-13-610729-X; ISBN-13: 978-0-13-610729-3 Recommended readings: Negnevitsky, M. Artificial Intelligence: A Guide to Intelligent Systems, 3/E, Addison-Wesley, 2011 ISBN-10: 1408225743 Turban, E., Aronson, King, D., J. E., Sharda, R.: Business Intelligence, Prentice Hall, 2008 (ISBN-10: 013234761X, ISBN-13: 9780132347617) Efraim Turban, Linda Volonino: Information Technology for Management, 8th Edition, ISBN 10 0-47091-680-X, Wiley. com, 2011 The eLearning site of the course will be available at: http://coo.uni-corvinus.hu/coospace |
| Course requirements and grading | Assignments: The major part of the classes will be based on individual or group problem solving and lab work. Students have to participate in computer lab work and based on case studies they have to write reports, prepare short assignments (papers with 2-4- pages) during (and after) the classes. Internet exercises and documented class work will be also evaluated. Exams The midterm and the final exam are written exams each lasting for 60 minutes. Both consist of 10 multiple choice test questions (worth each 1 point) and 4 essay questions (worth each 5 points). Each point equals 1 percent of the final grade. Assessment, grading: Grading 30% mid-term exam 30% final exam 40% assignment |
