Subject

Data Science

Course Title

Data Science
Data Science

Code

Digital Signal 0

Study Programme

Data science in computer science and engineering

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

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

Second cycle

Academic year / semester

9 / Winter

Number of ECTS credits

Teacher

Dimitar Trajanov, Igor Mishkovski, Miroslav Mirchev

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

The course covers the basic principles of supervised and unsupervised machine learning, as well as some advanced algorithmic paradigms. Students will be introduced to Deep Learning, NLP, and Causal Analysis concepts. The Explainable ML approach will be presented as a tool to understand and increase trust in ML models. The concepts of knowledge graphs and their application will be explained.

11.

Course content

Supervised Learning
Unsupervised Learning
Deep Learning
Introduction to NLP
Explainable Machine Learning
Causal analysis
Knowledge graphs

12.

Learning methods

Presentations, case studies....

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

45 + 30 + 30 + 15 + 60 = 180 hours

15.

Forms of teaching activities

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

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	60 hours

17.

Assessment method

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	0 points
17.4.	Final exam	0 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

NULL

20.

Language of instruction

English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.

Required literature

1. Aurélien Géron | Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems, 3rd edition | Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems | 2022
2. Robert Ness | Causal Machine Learning | Manning | 2022
3. Christoph Molnar | Interpretable Machine Learning | Independently published | 2022
4. Mayank Kejriwal, Craig A. Knoblock and Pedro Szekely | Knowledge Graphs Fundamentals, Techniques, and Applications | MIT Press | 2021

22.2.

Additional literature

No. Author Title Publisher Year