MK EN
ФИНКИ

Subject

Advanced Mathematical and Statistical Techniques

1. Course Title Advanced Mathematical and Statistical Techniques
Advanced mathematical and statistical techniques
2. Code Business Information - 03
3. Study Programme Bioinformatics
4. Organizer of the study programme (unit, institute, department or division) Faculty of Computer Science and Engineering
5. Degree level (first, second, third cycle) Second cycle
6. Academic year / semester 10 / Summer
7. Number of ECTS credits 6
8. Teacher Georchina Mircheva, Sonya Gievska
9. Prerequisites for enrolling in the course —
10. Objectives of the course programme (competences) The student will be able to use mathematical techniques for modeling and analysis of biological systems.
11. Course content This course covers the methods of statistical inference and stochastic modeling with applications in functional genomics and computational molecular biology. Computations will be applied using data from biological databases. The course structure will cover: statistical theory for sequence analysis and database searching, Markov models and Hidden Markov Models, elements of Bayesian and similarity inference, discrete data models, application of linear regression analysis, methods for multivariate data analysis (PCA, clustering), software tools for statistical calculations. Application of advanced deep learning techniques (graph neural networks, generative adversarial networks, transfer learning) for knowledge discovery in biomedical and bioinformatics data.
12. Learning methods Lectures supported by slide presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, invited guest lecturers, independent preparation and defense of a project assignment and seminar paper, learning in an electronic environment (forums, consultations).
13. Total available time 6 ECTS x 30 hours = 180 hours
14. Distribution of available time 60 + 0 + 45 + 45 + 30 = 180 hours
15. Forms of teaching activities
15.1. Lectures - theoretical instruction 60 hours
15.2. Exercises (laboratory, auditory), seminars, teamwork 0 hours
16. Other forms of activities
16.1. Project assignments 45 hours
16.2. Independent assignments 45 hours
16.3. Home study 30 hours
17. Assessment method
17.1. Tests 15 points
17.2. Seminar paper / project (presentation: written and oral) 45 points
17.3. Activities and learning 15 points
17.4. Final exam 0 points
18. Grading criteria (points / grade)
up to 50 points5 (five) (F)
from 51 to 60 points6 (six) (E)
from 61 to 70 points7 (seven) (D)
from 71 to 80 points8 (eight) (C)
from 81 to 90 points9 (nine) (B)
from 91 to 100 points10 (ten) (A)
19. Requirement for obtaining a signature and taking the final exam Activities completed 15
20. Language of instruction Macedonian and English
21. Method for monitoring the quality of teaching internal evaluation and survey mechanism
22. Literature
22.1. Required literature
1. Morris H. DeGroot, Mark J. Schervish | Probability and Statistics | Addison Wesley | 2001
2. Warren J. Ewens, Gregory Grant | Statistical Methods in Bioinformatics: An Introduction (Statistics for Biology and Health) | Springer | 2005
3. Laxmi Parida | Pattern Discovery in Bioinformatics: Theory & Algorithms | Chapman & Hall/CRC | 2007
4. Ian Goodfellow, Joshua Bengio, Aaron Courville | Deep Learning | MIT Press | 2015
5. William L. Hamilton | Graph Representation Learning | McGill | 2020
22.2. Additional literature
No. Author Title Publisher Year
Деканат Dean’s Office Студии Studies Акти Acts Проекти Projects