Curricula 2009 – 2010
Learning outcomes
Mastering basics of probability theory (probability, probability axioms, conditional probability, probability density function, cumulative distribution function, expectation, variance, discrete random variable, continuous random variable) and statistics (statistical experiment, descriptive statistics, inference statistics). Ability to calculate with complex numbers and matrices (also determinant, eigenvalues and eigenvectors), and define extremum values of a given function. Capability to analyze and solve differential equations.
Contents
Essential math and CS methods with applications to bioinformatics. The course content includes probability theory, statistics, complex numbers, matrices, ordinary differential equations, extremum values.
Teaching methods
| Teaching method | Contact | Online |
| Lectures | 12 h | 0 h |
| Exercises | 8 h | 0 h |
Teaching language
EnglishModes of study
Lectures, exercises, participation in classroom work (50% of home exercises must be completed) and Moodle activity (at lest 4 participations at the course discussion forum in Moodle), written exam.
Evaluation
Numeric 1-5.Recommended year of study
1. year autumnStudy materials
Lecture notes and supplementary books.
Further information
Organization responsible: Turku DIT
