Detailed time schedule

Week	Date	Topic(s)	Time	Location	Lecturer(s)
39	Sep 22	Introduction. General description of the course. Computational Statistics - the statistician's point of view and the numerical analyst's point of view	9:15-12:00	KR	DVR, LE
		The statistical language R - short introduction. Computer lab exercises	13:15-17:00	KR	MN
	Sep 23	Regression analysis, statistical concepts. Least Squares and QR factorization	9:15-12:00	KR	DVR, LE
		Computer lab: Multiple regression. Normal equations vs QR factorization. Polynomial regression	13:15-17:00	KR	DVR, LE
	Sep 24	Regression analysis (cont). Rank deficiency. Singular value decomposition (SVD). Pseudo-inverses.	9:15-12:00	KR	DVR, LE
		Computer lab: Numerical rank deficiency, collinearity. Application in pattern recognition: classification of handwritten digits (regression)	13:15-17:00	KR	LE
	Sep 25	Regression problems with sparse data matrices. Sparse matrices - storage formats. Solving least squares problems with sparse matrices: direct and iterative methods. Computing the SVD	9:15-12:00	KR	MN
		Computer lab: Handling sparse matrices in R and MATLAB. Regression. Text mining	13:15-17:00	KR	MN
	Sep 26	Graphs and their usage in Statistical applications (page-rank), regression trees, classification trees. Concepts of numerical stability. Floating point computations - short introduction, variance example	9:15-12:00	KR	LE, DVR
		Computer lab: Floating point arithmetic - examples of loss of accuracy. Page ranking, the Google matrix	13:15-17:00	KR	LE