# PřF:Bi7441 Scientific comput. in biology - Course Information

## Bi7441 Scientific computing in biology and biomedicine

Faculty of Science
spring 2018
Extent and Intensity
2/1. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Vlad Popovici, PhD (lecturer)
Supervisor
Research Centre for Toxic Compounds in the Environment (RECETOX) - Chemistry Section - Faculty of Science
Contact Person: doc. Ing. Vlad Popovici, PhD
Supplier department: Research Centre for Toxic Compounds in the Environment (RECETOX) - Chemistry Section - Faculty of Science
Prerequisites
Basic linear algebra, notions of optimization theory, Matlab and R programming
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
The course aims at introducing the students to the principles of scientific computing through practical examples. The course will present numerical methods for linear algebra and optimization, and will discuss implementation issues, including notions of parallel computing in Matlab and R.
Learning outcomes
At the end of the course, students should be able to: -Understand the basics of numerical methods for linear algebra; -Know and have experience in applying methods in computational statistics; -Gain knowledge and experience of computer-intensive methods for data analysis; -Know how to use parallel computation tools; -Apply the theory in practice for solving problems in biological data analysis, using Matlab and R
Syllabus
• Introduction: data representation; approximations and errors; computing platforms: from desktop to cloud computing • Systems of linear equations: triangular systems; Gauss elimination; norms and conditioning. • Linear least squares: normal equations; orthogonalizations • Eigendecompositions and singular values: eigenvalues, eigenvectors; singular value decomposition • Optimization: general topics; one-dimensional; multidimensional Monte Carlo methods: random numbers; simulation, sampling and non-parametric statistics • Bootstrapping and resampling: bootstrap as an analytical tool; confidence intervals from bootstrapping • Smoothing and local regression techniques: linear smoothing; smoothing and bootstrapping • Parallel computing: levels of parallelism; platforms for computational biology; applications in computational biology
Literature
recommended literature
• KEPNER, Jeremy. Parallel MATLAB for Multicore and Multinode Computers. 1. vyd. : SIAM-Society for Industrial and Applied Mathematics, 2009. ISBN 978-0-89871-673-3. info
• Handbook of computational statistics : concepts and methods. Edited by James E. Gentle - Wolfgang Härdle - Yuichi Mori. London: Springer, 2004. xii, 1070. ISBN 3540404643. info
• HEATH, Michael T. Scientific Computing. An introductory survey. 2nd. : The McGraw-Hill Companies, Inc., 2002. ISBN 0-07-239910-4. info
Teaching methods
Lectures, homeworks and practical exercises
Assessment methods
Weekly lectures complemented by practical exercises and short homeworks. Written and practical exam.
Language of instruction
English