PřF:MF006 Seminary on Fin. Mathematics - Course Information
MF006 Seminary on Financial Mathematics
Faculty of ScienceSpring 2015
- Extent and Intensity
- 0/2. 2 credit(s) (příf plus uk k 1 zk 2 plus 1 > 4). Type of Completion: z (credit).
- Teacher(s)
- Mgr. Jana Svobodová (lecturer)
- Guaranteed by
- prof. RNDr. Ivanka Horová, CSc.
Department of Mathematics and Statistics – Departments – Faculty of Science
Supplier department: Department of Mathematics and Statistics – Departments – Faculty of Science - Timetable of Seminar Groups
- MF006/01: Tue 16:00–17:50 M6,01011
- Course Enrolment Limitations
- The course is offered to students of any study field.
- Course objectives
- Topics for the seminar will be levied on the practical problems being solved in financial institutions. Among the topics are, for example: Methods for Exploratory data analysis, application of logistic regression models, Bayesian models, neural networks and other methods used in financial practise.
- Syllabus
- Methods for Exploratory data analysis,
- Application of logistic regression models,
- Bayesian models,
- Neural networks,
- Decision trees.
- Literature
- ANDERSON, Raymond. The credit scoring toolkit : theory and practice for retail credit risk management and decision automation. 1st pub. Oxford: Oxford University Press, 2007, lvi, 731. ISBN 9780199226405. info
- SIDDIQI, Naeem. Credit risk scorecards : developing and implementing intelligent credit scoring. Hoboken, N.J.: Wiley, 2006, xi, 196. ISBN 047175451X. info
- THOMAS, L. C., David B. EDELMAN and Jonathan N. CROOK. Credit scoring and its applications. Philadelphia, Pa.: Society for Industrial and Applied Mathematics, 2002, xiv, 248. ISBN 0898714834. URL info
- WEST, Mike and Jeff HARRISON. Bayesian forecasting and dynamic models. 2nd ed. New York: Springer, 1997, xiv, 680. ISBN 0387947256. info
- BISHOP, Christopher M. Neural networks for pattern recognition. 1st pub. Oxford: Oxford University Press, 1995, xvii, 482. ISBN 0198538499. info
- Teaching methods
- Exercises-application of methods on selected data, data processing in MS Excel, Matlab and Clementine/SAS.
- Assessment methods
- project - 100% correct is needed to acknowledge
- Language of instruction
- Czech
- Further Comments
- Study Materials
The course is taught annually.
- Enrolment Statistics (Spring 2015, recent)
- Permalink: https://is.muni.cz/course/sci/spring2015/MF006