Machine learning

Course title

Machine learning

Code

F18L3S036

Study programme

Computer Science (4 years)

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

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First cycle

Academic year / semester

6 / Summer

Number of ECTS credits

Teacher

—

Prerequisites for enrolling in the course

Веројатност и статистика или Бизнис статистика

10.

Course objectives (competences)

The aim of the course is for the students to become familiar with the basics of modern machine learning techniques. Upon completion of the course, candidates will have deeper knowledge of advanced technologies and methods of machine learning; they will be able to understand, analyze and formulate general problems in the field of machine learning; they can successfully apply algorithms for machine learning in solving real problems; will be able to conceptualize, analyze, realize and evaluate the performance of a machine learning system.

11.

Course content

Introduction to Machine Learning, Generative Models, Gaussian Models, Univariate and Multivariate Linear Regression, Logistic Regression (Hypothesis Representation, Cost Functions, Error Evaluation, Model Selection and Validation), Unsupervised Learning, Mixed Models and EM Algorithm, Kernel methods, support vector machines, Neural networks, regularization in neural networks, Classification and regression decision trees, 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 the available time

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

15.

Forms of teaching activities

15.1.	Lectures – theoretical teaching	30 hours
15.2.	Exercises (laboratory, auditory), seminars, team work	45 hours

16.

Other forms of activities

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

17.

Grading

17.1.	Tests	10 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activity and participation	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.

Requirements for signature and taking the final exam

Activities 15.2 and 16.1 have been completed.

20.

Language of instruction

Macedonian and English

21.

Method of monitoring teaching quality

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature No. Author Title Publisher Year
22.2.	Additional literature No. Author Title Publisher Year