E7441 Scientific computing in biology and biomedicine

Faculty of Science
Spring 2024
Extent and Intensity
2/1/0. 3 credit(s) (plus extra credits for completion). Type of Completion: zk (examination).
Teacher(s)
doc. Ing. Vlad Popovici, PhD (lecturer)
Guaranteed by
doc. Ing. Vlad Popovici, PhD
RECETOX – Faculty of Science
Contact Person: doc. Ing. Vlad Popovici, PhD
Supplier department: RECETOX – Faculty of Science
Timetable
Mon 19. 2. to Sun 26. 5. Mon 11:00–13:50 D29/347-RCX2
Prerequisites
Basic linear algebra, notions of optimization theory, numerical methods, Python and R programming
Course Enrolment Limitations
The course is offered to students of any study field.
Course objectives
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 Python (and R)
Learning outcomes
After completing the course, a student will be able to:
-use the appropriate methods for solving various types of systems of linear equations
-identify the major sources of numerical instability and take steps for correcting
-solve numerically basic optimization problems;
-use Monte-Carlo methods for parameter estimation;
-exploit the parallelism for better use of computations resources;
-identify the suitable numerical routines for solving the given problem
Syllabus
  • Introduction: data representation; approximations and errors;
  • 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
  • Parallel computing: levels of parallelism; platforms for computational biology; applications in computational biology
  • Support material:
  • KONG Q., SIAUW T., BAYEN A. (2020). Python programming and numerical methods. Academic Press. ISBN: 9780128195499
  • HEATH M.T. (2002). Scientific Computing. An introductory survey. McGraw-Hill, 2nd edition. ISBN: 0-07-239910-4
  • GENTLE J.E. (2005). Elements of Computational Statistics. Springer. ISBN:978-0387954899
Teaching methods
lectures; class discussion; hands-on computer exercises; homework
Assessment methods
continuous assessment throughout the semester; written and practical exam.
Language of instruction
English
Further Comments
Study Materials
The course is taught annually.
The course is also listed under the following terms Spring 2023, Spring 2025.
  • Enrolment Statistics (recent)
  • Permalink: https://is.muni.cz/course/sci/spring2024/E7441