Data Mining and Statistical LearningDF21200, 2012HT
No of lectures
14x2 hours + 12 computer labs
Ph.D. students interested in data mining and related topics
The course was last given
Provide insight into the statistical foundations of data mining and related techniques. Provide practical experience of data-driven methods for prediction and classification.
A total of at least 1.5 years of full-time studies in mathematics, statistics and computer science. At least one basic course in statistics and computer science, respectively. Basic courses in calculus and linear algebra.
One lecture (2h) per week. Computer labs almost every week
Regression methods (ridge regression, partial least squares analysis)
Smoothing techniques (kernel smoothing, splines)
Generalized additive models
Artificial neural networks
Hastie, T., Tibshirani, R., Friedman, J. The Elements of Statistical Learning. second edition, Springer-Verlag, 2009. ISBN:0-387-84857
Individual reporting of computer labs
Page responsible: Director of Graduate Studies