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 self study (March 14-18, 2016):

• 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: Intro   QR   PLS   Hands-on session
• Maya Neytcheva: SVD   Pseudo-inverses     Sparse matrices     Lab 1   Data for Lab1     Computing in parallel     Lab 2   Data for Lab2

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
March 21	Introduction. General description of the course	9:00-9:15	2344	MN
	Computational Statistics - the statistician's point of view	9:15-11:00	2344	DvR
	Regression analysis, statistical concepts	11:15-12:00	2344	DvR
	Computational Statistics - the numerical analyst's point of view	13:15-14:00	2344	LE
	Least Squares and QR factorization. Normal equations vs QR factorization.	14:15-16:00	2344	LE
March 22	Singular value decomposition (SVD). Pseudo-inverses. Applications	9:00-11:00	2344	MN
	Regression problems leading to sparse matrices. Sparse matrices - storage formats.	11:15-12:00	2344	MN
	Solving least squares problems with sparse matrices: direct and iterative methods. Computing the SVD	13:15-14:00	2344	MN
	'Hands-on' session: QR, ill-conditioning, SVD, large scale problems	15:15-17:00	2344	MN
March 23	Discussion of the experience from the last computer lab	9:00-9:15	2344	MN
	Principal Component Analysis (PCA)	9:15-12:00	2344	DvR
	Handling sparse matrices in R and MATLAB. Briefly on Parallel Statistical computing.	13:15-15:00	2344	MN
	'Hands-on' session	15:15-17:00	2344	DvR MN
March 24	Discussion of the experience from the last computer lab	9:00-9:15	2344	MN
	Partial Least Squares (PLS)	9:15-12:00	2344	LE
	'Hands-on' session: PCA, PLS	13:15-17:00	2344	LE

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 .