This course is organized by the Swedish e-Science Education program (SeSE).

It consists of three parts - self reading/lectures, labs/project preparation. The lectures and the labs will be given at the Department of Information Technology, Uppsala University.

Review material for self study (September 16-20, 2019):

• Linear systems, rank, singular matrix, non-singular matrix, tridiagonal (band) matrix, LU, and Gaussian elimination.
  Read:
  • (Wikipedia source) or some other introductor text.
• Basic knowledge of stochastic variables, statistical distributions. Read
  • (Wikipedia source)
  • or some other introductor text.
• Those who want to learn R well and use it in the course, should carefully study one of the two suggested books ([1] or [2])

Course materials (to be updated during the course)

• Lars Eldén: (Course material to be added)
• Dietrich von Rosen: (Course material to be added)
• Maya Neytcheva: (Course material to be added)

Find here nformation on the project work to be done in order to pass the course with full credit

Detailed time schedule PRELIMINARY! Some redistribution of the topics per day is possible.

Date	Topic(s)	Time	Location	Lecturer
September 23	Introduction. General description of the course	9:00-9:15	1510	MN
	Computational Statistics - the statistician's point of view	9:15-11:00	1510	DvR
	Regression analysis, statistical concepts	11:15-12:00	1510	DvR
	Computational Statistics - the numerical analyst's point of view	13:15-14:00	2345	LE
	Regression: Least Squares and QR factorization. Normal equations vs QR factorization	14:15-16:00	2345	LE
September 24	Regression problems leading to sparse matrices	9:00-11:00	2345	MN
	Solving least squares problems with sparse matrices: direct and iterative methods	11:15-12:00	2345	MN
	Sparse matrices - storage formats. Handling sparse matrices in R and MATLAB	13:15-15:00	2446	MN
	'Hands-on' session: QR, ill-conditioning, large scale problems	15:15-17:00	2446	MN
September 25	Discussion of the experience from the last computer lab	9:00-9:15	2414b	MN
	Principal Component Analysis (PCA)	9:15-12:00	2414b	LE
	Partial Least Squares (PLS)	13:15-15:00	1510	LE
	'Hands-on' session: PCA, PLS	15:15-17:00	1510	LE
September 26	Discussion of the experience from the last computer lab	9:00-9:15	1211	MN
	Singular value decomposition and how to compute it	9:15-12:00	1211	MN
	Applications of SVD	13:15-15:00	2245	DvR, MN
	'Hands-on' session: SVD	15:15-17:00	2245	DvR, MN
September 27	Discussion of the experience from the last computer lab	9:00-9:15	1111	MN
	Pseudo-inverses. Applications	9:15-12:00	1111	DvR

Recommended books:

1. James E. Gentle, Computational Statistics, Springer, 2009.
2. Lars Eldén. Matrix Methods in Data Mining and Pattern Recognition. SIAM, Philadelphia, PA, Philadelphia, PA, USA, 2007.

1. Peter Dalgaard, Introductory Statistics with R, Springer, 2002.
2. W.John Braun, Duncan J. Murdoch, A First Course in Statistical Programmimg with R, Cambridge University Press, 2007.
3. Geof H. Givens and Jennifer A. Hoeting, Computational Statistics, Wiley, 2005.
4. Wendy L. Martinez and Angel R. Martinez, Computational Statistics Handbook with MATLAB, Chapman & Hall/CRC, 2002.

Organization issues:
Some instructions how to find us in Uppsala are to be found here .
Suggested hotel to book rooms in Uppsala: Hotel Uppsala .