IAM557 - Statistical Learning and Simulation

Credit: 3(3-0); ECTS: 8.0
Instructor(s): Bülent Karasözen / Gerhard-Wilhelm Weber
Prerequisites: Consent of Instructor(s)

Course Catalogue Description

Introduction to statistical learning, simulation and supervised learning. Linear methods of regression and classification. Model assessment and selection. Model inference and averaging. Additive models, trees and related methods. Prototype methods and nearest neighbors. Cluster algorithms and support vector machines. Unsupervised learning. Computer applications and MATLAB exercises are important elements of the course.

Course Objectives

• The objective of this course is to provide students with theory, methods and practice in data mining, inference, prediction and information. During and after the course, a deepening and continuation is offered by projects, e.g., in financial mathematics (identification of financial processes, prediction of credit default, loan banking, risk management), in data/information processing and technology, in computational biology, medicine, bioinformatics, biotechnology and the sectors of environment and development.

Course Learning Outcomes

• At the end of the course students should have a good overview of modern methods in statistical learning and simulation. They should also be able to choose and, by calculation and simulation, work them out appropriately in contexts of applications.

Tentative (Weekly) Outline

1. Introduction into statistical learning and simulation
2. Introduction into supervised learning
3. Linear methods of regression, and elements of MATLAB
4. Linear methods of regression
5. Linear methods of classification
6. Linear methods in classification, and Model assessment and selection
7. Model assessment and selection
8. Model inference and averaging
9. Model infer. & aver., and Additive models, trees and related methods
10. Additive models, trees and related methods
11. Prototype methods and nearest neighbours
12. Prot. Meth. & n. neighb., and Cluster algor. & support vector machines
13. Unsupervised learning, Term Projects
14. Conclusions and Outlook

Course Textbook(s)

• N. Christianini and J. Shawe-Taylor, “An Introduction to Support Vector Machines”, Cambridge University Press, 2000

Supplementary Materials and Resources

• Books:
• Th. M. Cover and J.A. Thomas, “Elements of Information Theory”, Wiley Series in Communication, 1991
• T. Hastie, R. Tibshirani and J. Friedman, “The Elements of Statistical Learning”, Springer Series in Statistics, second edition, Springer, 2009
• Readings:
• M. Laetsch, “Distance between Strings and Its Application to Amino Acid Sequences – An Information Theoretic Approach”, diploma thesis, Chemnitz University of Technology, Department of Mathematics
• T.G. Oberstein, “Efficient Training of Observable Operator Models using Context Graphs”, diploma thesis, University of Cologne, Institute of Mathematics, GMD Report, 2001.
• Resources:
• Lecture Notes: During the course, lecture notes (a manuscript) will be distributed. Furthermore, in lectures and exercises there will be further valuable appendices (e.g., elements of probability and statistics) and modern texts offered for updating basic knowledge and for treating interesting mini-projects, respectively.
• MATLAB Student Version is available to download on MathWorks website, http://www.mathworks.com, or METU FTP Servers (Licenced)

More Info on METU Catalogue

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