Subject

Applied Machine Learning

Course Title

Applied Machine Learning
Applied Machine Learning

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

10 / Summer

Number of ECTS credits

Teacher

Aleksandra Dedinec, Andrea Kulakov, Miroslav Mirchev, Sonja Gievska

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

Applied Machine Learning teaches students some of the principal ideas in machine learning and data science, taking them from a real business problem to a functional AI solution deployed at scale.
The primary focus is on building real-world AI solutions by applying the skills acquired in the first semester. The emphasis is on practical knowledge rather than on mathematical and theoretical foundations. In balancing theory and practice, priority will be given to the practical and applied aspects of machine learning.

11.

Course content

Machine Learning Operations
Automated Machine Learning
Parallelisation of Machine Learning
Machine Learning in the Cloud
Prescriptive Analytics
Time-Series Analysis
Introduction to Computer Vision
Introduction to Machine Learning for Audio and Speech

12.

Learning methods

Presentations, surveys, etc.

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

45 + 30 + 30 + 30 + 55 = 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	30 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	55 hours

17.

Assessment method

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	30 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. David Forsyth \| Applied Machine Learning \| Springer \| 2019 2. Taweh Beysolow \| Applied Reinforcement Learning with Python: With OpenAI Gym, Tensorflow, and Keras \| Apress \| 2019 3. Jeff Prosise \| Applied Machine Learning and AI for Engineersbooks \| O'Reilly \| 2022 4.
22.2.	Additional literature No. Author Title Publisher Year