Subject

Data Science for the Internet of Things

Course Title

Data Science for the Internet of Things
Data science in the Internet of Things

Code

m23_s_049

Study Programme

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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

Georgi Madjarov, Igor Mishkovski, Miroslav Mirchev

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

The goal of the course is to equip students with the skills to perform detailed analysis and knowledge discovery from sensor data from multiple sources in the Internet of Things, and to use advanced machine learning algorithms to solve various problems such as classification, regression, and clustering.

11.

Course content

Advanced machine learning methods for supervised, semi-supervised, and unsupervised learning, such as deep neural networks, decision tree ensembles, kernel methods, etc. Techniques for signal processing, data cleansing, attribute selection, and sensor data fusion in the Internet of Things. Adaptation of data acquisition systems and communication flows to current conditions in real time. Analysis, prediction, and classification of time series data. Ambient intelligence and pervasive computing. Use of software tools for knowledge storage and discovery from massive data. Case studies: human activity recognition, environmental monitoring, natural disaster early warning systems, industrial IoT systems, and others.

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, practical exercises, teamwork, case studies, guest speakers, independent project work and term papers, and e-learning.

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

45 + 15 + 30 + 50 + 40 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	45 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	15 hours

16.

Other forms of activities

16.1.	Project assignments	50 hours
16.2.	Independent assignments	30 hours
16.3.	Home study	40 hours

17.

Assessment method

17.1.	Tests	45 points
17.2.	Seminar paper / project (presentation: written and oral)	50 points
17.3.	Activities and learning	10 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

Activities 15 and 16 completed

20.

Language of instruction

Macedonian or English

21.

Method for monitoring the quality of teaching

Internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature 1. Murphy, Kevin P. \| Machine learning: a probabilistic perspective \| MIT press \| 2012 2. François Chollet \| Deep learning with Python \| Manning publications \| 2021 3. Edited by John Davies, Carolina Fortuna \| The Internet of Things: From Data to Insight \| Wiley \| 2020 4. John D. Kelleher, Brendan Tierney \| Data science \| MIT press \| 2018
22.2.	Additional literature No. Author Title Publisher Year