Subject

Machine learning

Course Title

Machine learning
Machine learning

Code

F23L3S036

Study Programme

Computer Science, Bioinformatics

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

6 / Summer

Number of ECTS credits

Teacher

Alexandra Dedinec, Miroslav Mirchev

Prerequisites for enrolling in the course

Probability and Statistics or Business Statistics or Mathematics 3

10.

Objectives of the course programme (competences)

The goal of the course is for students to become familiar with the fundamentals of modern techniques in the field of machine learning. Upon completion of the course, candidates will: have in-depth knowledge of advanced technologies and methods for machine learning; be able to understand, analyze, and formulate general problems in the field of machine learning; will be able to successfully apply machine learning algorithms to solve real-world problems; will be able to design, analyze, implement, and evaluate the performance of a machine learning system.

11.

Course content

Lectures:
1. Introduction to Machine Learning
2. Generative models
3. Gaussian models
4. Linear Regression with One or More Variables
5. Logistic Regression
6. Unsupervised learning, mixed models, and the EM algorithm
7. Kernel methods, machines with carrying vectors
8. Neural networks
9. Classification and Regression Trees
10. Deep learning

Practical Classes:
1. Introduction to Machine Learning
2. Generative models
3. Gaussian models
4. Linear Regression with One or More Variables
5. Logistic Regression
6. Unsupervised learning, mixed models, and the EM algorithm
7. Kernel methods, machines with carrying vectors
8. Neural networks
9. Classification and Regression Trees
10. Deep learning

12.

Learning methods

Lectures using presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defense of a project assignment and a seminar paper.

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	10 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	10 points
17.4.	Final exam	70 points

18.

Grading criteria (points / grade)

up to 50 points	5 (five) (F)
from 51 to 60 points	6 (six) (E)
from 61 to 70 points	7 (seven) (D)
from 71 to 80 points	8 (eight) (C)
from 81 to 90 points	9 (nine) (B)
from 91 to 100 points	10 (ten) (A)

19.

Requirement for obtaining a signature and taking the final exam

Activities 15.2 and 16.1 have been completed.

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. Christopher M. Bishop \| Pattern Recognition and Machine Learning \| Springer \| 2006 2. Kevin P. Murphy \| Machine Learning - A Probabilistic Perspective \| MIT Press \| 2012 3. Aurélien Géron \| Hands-on Machine Learning with Scikit-Learn, Keras & TensorFlow \| O'Reilly Media \| 2019
22.2.	Additional literature No. Author Title Publisher Year