Course ObjectiveThe course covers all important aspects of data modeling with a number
of methods and techniques. The emphasis is on both theory and practice.
The objective is to help the students to develop a deep understanding of
the methods, recognize their strengths and limitations, and acquire the
practical skills to analyze complex datasets and critically interpret
Course ContentThis course covers:
(1) statistical methods for analyzing data;
(2) machine learning techniques;
(3) efficient implementation of statistical and machine learning
(4) validity and methodological aspect of data analysis.
Teaching MethodsLectures and tutorials
Method of AssessmentExam and group assignment
Entry RequirementsBasic knowledge of probability, statistics and programing.
- Friedman, J., Hastie, T., & Tibshirani, R. (2009), The Elements of
Statistical Learning: Data
Mining, Inference, and Prediction (2nd edition). Springer Series in
- Hardle, W. K., Muller, M., Sperlich, S., & Werwatz, A. (2012),
Nonparametric and semipara-
metric models. Springer Science & Business Media.
|Language of Tuition||English|
|Faculty||School of Business and Economics|
|Course Coordinator||dr. P. Gorgi|
|Examiner||dr. P. Gorgi|
dr. P. Gorgi
You need to register for this course yourself
Last-minute registration is available for this course.
|Teaching Methods||Lecture, Study Group|
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