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Subject

Bayesian Data Analysis

1. Course Title Bayesian Data Analysis
Bayesian data analysis
2. Code m23_w_009
3. Study Programme Data statistics and analytics
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 9 / Winter
7. Number of ECTS credits 6
8. Teacher Билјана Тојтовска Рибарски, Бојан Илијоски
9. Prerequisites for enrolling in the course —
10. Objectives of the course programme (competences) Целта на овој предет е студентите да се запознаат со концепти од Бајесова статистика и да научат да ги применуваат на реални проблеми и различни податочни множества. Студентот ќе се запознае со методи на симулација и ќе научи да ги толкува резултатите од Бајесовата анализа. Анализата на податоците се прави во R и/или Python
11. Course content Бејесово правило, prior, веродостојност, posterior дистрибуција. Модели за дискретни/непрекинати типови на податоци. Коњугирани фамилии (beta, gamma-Poisson, normal-normal). Предвидување на идни настани. Бејесова регресија. Loss фукција. Теорија на одлучување.
Monte Carlo апроксимации. Апостериории апроксимации со Gibbs семплер. MCMC (Monte Carlo Markov chain). Бајесови алгоритми во Машинско учење и нивна примена на различни податочни множества. Вовед во хиерархиско моделирање.
12. Learning methods Lectures, projects, discussions and workshops
13. Total available time 6 ECTS x 30 hours = 180 hours
14. Distribution of available time 60 + 0 + 60 + 45 + 75 = 180 часа
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 60 hours
16.3. Home study 75 hours
17. Assessment method
17.1. Tests 60 points
17.2. Seminar paper / project (presentation: written and oral) 45 points
17.3. Activities and learning 10 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 15 and 16 implemented
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. Cameron Davidson-Pilon | Bayesian Methods for Hackers: Probabilistic Programming and Bayesian Inference | Addison-Wesley Data & Analytics | 2015
2. John Kruschke | Doing Bayesian Data Analysis: A Tutorial with R, JAGS, and Stan | Elsevier, Academic Press | 2015
3. Joel Grus | Data Science from Scratch (first principles with Python) | O`Reilly | 2015
4. Kandethody M.Ramachandran, Chris P.Tsokos | Mathematical Statistics with Applications | Elsevier, Academic Press | 2009
22.2. Additional literature
No. Author Title Publisher Year
Деканат Dean’s Office Студии Studies Акти Acts Проекти Projects