Bi7441 Scientific computing in biology and biomedicine

Faculty of Science
Spring 2020

The course is not taught in Spring 2020

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)
Guaranteed by
prof. RNDr. Ladislav Dušek, Ph.D.
RECETOX – Faculty of Science
Contact Person: doc. Ing. Vlad Popovici, PhD
Supplier department: RECETOX – Faculty of Science
Prerequisites
Basic linear algebra, notions of optimization theory, Matlab and R programming
Course Enrolment Limitations
The course is also offered to the students of the fields other than those the course is directly associated with.
fields of study / plans the course is directly associated with
Course objectives
The aim of the course is to introduce students to the basics of numerical methods for linear algebra, to learn them how to apply methods of computational statistics and computer-intensive methods for data analysis including parallel computation tools. Students will also learn how to apply the theory in practice for solving problems in biological data analysis.
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. 1st ed. 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. Berlin: 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
Further Comments
The course is taught annually.
The course is taught every week.
The course is also listed under the following terms Spring 2015, Spring 2016, Spring 2017, spring 2018, Spring 2019.
  • Enrolment Statistics (Spring 2020, recent)
  • Permalink: https://is.muni.cz/course/sci/spring2020/Bi7441