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

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

Review material for Week 38's self study:

• Linear systems, rank, singular matrix, non-singular matrix, tridiagonal (band) matrix, LU, and Gaussian elimination.
  Read:
  • Eldén, Wittmeyer-Koch, Bruun-Nielsen, Introduction to numerical computations, Studentlitteratur, 2001, (here)
  • (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)

• Lars Eldén: Slides and Labs
• Dietrich von Rosen: Slides
• Maya Neytcheva: Slides and Labs

Information on the project work to be done in order to pass the course with full credit

Detailed time schedule

Date	Topic(s)	Time	Location	Lecturer
Sep 22	Introduction. General description of the course	9:15-9:30	2414b	MN
	Computational Statistics - the statistician's point of view	9:30-11:00	2414b	DvR
	Computational Statistics - the numerical analyst's point of view	11:15-12:00	2414b	LE
	Least Squares and QR factorization. Normal equations vs QR factorization.	13:15-15:00	2414b	LE
	Regression analysis, statistical concepts	15:15-16:00	2414b	DvR
Sep 23	Singular value decomposition (SVD). Pseudo-inverses. Applications	9:15-12:00	2414b	MN
	Computer lab and 'Hands-on' session: QR, ill-conditioning, SVD, large scale problems	13:15-17:00	2315	MN
Sep 24	Discussion of the experience from the last computer lab	9:15-9:30	2414b	MN
	Principal Component Analysis (PCA)	9:30-12:00	2414b	DvR
	Computer lab and 'Hands-on' session: ...	13:15-17:00	2315	DvR MN
Sep 25	Discussion of the experience from the last computer lab	9:15-9:30	2414b	MN
	Partial Least Squares (PLS)	9:30-12:00	2414b	LE
	Computer lab and 'Hands-on' session: PCA, PLS	13:15-17:00	1312	LE
Sep 26	Discussion of the experience from the last computer lab	9:15-9:30	2414b	MN
	Regression problems leading to sparse matrices. Sparse matrices - storage formats. Solving least squares problems with sparse matrices: direct and iterative methods. Computing the SVD	9:30-12:00	2414b	MN
	Handling sparse matrices in R and MATLAB. Briefly on Parallel Statistical computing. Summary of the course material.	13:15-15:00	2414b	MN

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 .