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SYLLABUS

Data Mining and Statistical Learning, 15 ECTS Credits

COURSE CATEGORY   Master´s Programme in Statistics and Data Mining MAIN FIELD OF STUDY   Statistik - STA SUBJECT AREA     COURSE CODE   732A33

AIM OF THE COURSE

The course lays the foundation for professional work and research in which large amounts of data are explored, modified, modelled and assessed to uncover previously unknown patterns and trends. Having completed the course, the student should be able to: - account for the principles of statistical modeling, in particular for the analysis of large data sets - utilize tools in SAS environment to explore large and complex data sets, derive data-based models, assess their outcomes and use such models for forecasting - compare the performance of statistical and data mining models in order to select the most appropriate model in a given context

CONTENTS

- basic concepts in statistical learning, in particular supervised learning, - model selection strategies involving the use of training sets, validation sets, and test sets and model selection by cross-validation. - linear regression technique and regression shrinkage methods - spline smoothers and kernel smoothers, - decision trees and classification methods, such as discriminant analysis and logistic regression, - neural networks, support vector machines, and generalized additive models - ensemble methods, including bagging and boosting. - Bayesian approach in data mining

TEACHING

The teaching comprises lectures, seminars, and computer exercises. Lectures are devoted to presentations of theories, concepts and methods. Computer exercises provide practical experience of data analysis in SAS environment (as a rule) or in other enviroments (in exceptional cases). The seminars comprise student presentations and discussions of computer assignments. Language of instruction: English.

EXAMINATION

Written reports on the computer assignments. Obligatory attendance of the seminars. One final written examination. Students failing an exam covering either the entire course or part of the course two times are entitled to have a new examiner appointed for the reexamination. Students who have passed an examination may not retake it in order to improve their grades.

ADMISSION REQUIREMENTS

For acceptance to the course, the student must have a bachelor’s degree with a total of at least 90 ECTS credits (1.5 years of full-time studies) in mathematics, applied mathematics, statistics, and computer science. The undergraduate courses in mathematics should include both calculus and linear algebra. Basic undergraduate courses in statistics and computer science are also required. Documented knowledge of English equivalent to Engelska B/Engelska 6. internationally recognized test, e.g. TOEFL (minimum scores: Paper based 575 + TWE-score 4.5, and internet based 90), IELTS, academic (minimum score Overall band 6.5 and no band under 5.5), or equivalent.

GRADING

The course is graded according to the ECTS grading scale A-F

CERTIFICATE

Course certificate is issued by the Faculty Board on request. The Department provides a special form which should be submitted to the Student Affairs Division.

COURSE LITERATURE

The course literature is decided upon by the department in question.

OTHER INFORMATION

Planning and implementation of a course must take its starting point in the wording of the syllabus. The course evaluation included in each course must therefore take up the question how well the course agrees with the syllabus. The course is carried out in such a way that both men´s and women´s experience and knowledge is made visible and developed.

Data Mining and Statistical Learning Data Mining and Statistical Learning   Department responsible for the course or equivalent: IDA - Department of Computer and Information Science             Registrar No: 1330/06-41   Course Code: 732A33           Exam codes: see Local Computer System       Subject/Subject Area : Statistik - STA                       Level   Education level     Subject Area Code   Field of Education   A1X   Advanced level     STA   SA   The syllabus was approved by the Board of Faculty of Arts and Science 2008-09-10 Latest revision 2013-03-18