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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**- prof. RNDr. Ladislav Dušek, Ph.D.

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**- 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

*recommended literature*- KEPNER, Jeremy.
**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
**Further Comments**- Study Materials

The course is taught annually.

The course is taught: every week.

- Enrolment Statistics (spring 2018, recent)
- Permalink: https://is.muni.cz/course/sci/spring2018/Bi7441