Data Mining and Statistical LearningDF21200, 2008HTFull
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Course plan
No of lectures
25 x 2h
Recommended for
Doctoral students with a special interest in analysis of large and complex data sets
The course was last given
2007HT
Goals
Prerequisites
Undergraduate studies with a minimum of 90 ECTS credits in mathematics, statistics and computer science together and at least one course in each of the following topics: linear algebra, statistical inference and computer science.
Organization
Lectures and computer labs
Contents
Supervised learning (classification and prediction). Model selection. Regression methods. Additive models. Neural networks. Support vector machines.
Literature
Hastie, Tibshirani & Friedman. The elements of statistical learning. Springer.
Lecturers
Anders Grimvall, Oleg Sysoev
Examiner
Anders Grimvall
Examination
Lab reports and final written examination
Credit
12 HEC (Higher Education Credits)
Comments
The course will be coordinated with 732A20
www.ida.liu.se/~732A20
Page responsible: Director of Graduate Studies