Data science in Internet of things

1.

Subject title

Data science in Internet of things

Наука за податоците во Интернет од нештата

2.

Code

m23_s_049

3.

Study program

Cloud Computing, IT management, Security, Cryptography and Coding, Еducation with ICT, Eco-informatics, Internet Technologies and cyber security, Computer Science, Statistics and Data Analytics, Cloud Computing, IT management, Bioinformatics, Security, Cryptography and Coding, Statistics and Data Analytics, Bioinformatics, Inteligent Systems, Software for embedded systems, Data science in computer science and engineering, Software Engineering, Software Engineering,

4.

Organizer of the study program (unit, institute, department, division)

Faculty of Information Sciences and Computer Engineering

5.

Study cycle (first, second, third)

Втор циклус

6.

Academic year / semester

5 / Летен

7. Number of ECTS credits

6.0

8.

Instructor

проф. д-р Ѓорѓи Маџаров проф. д-р Игор Мишковски ворн. проф. д-р Мирослав Мирчев

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

The goal of the course is to teach the students to perform detailed analysis and knowledge discovery from sensor data from multiple sources in Internet of things and apply advanced machine learning algorithms for solving various problems like classification, regression and clustering.

11.

Subject content:

Advanced machine learning methods for supervised, semi-supervised and unsupervised learning, like deep neural networks, ensembles of decision trees, kernel methods etc. Techniques for signal processing, data cleansing, feature selection, and sensor data fusion in the Internet of things. Adaptation of the systems for data collection and processing in real time. Analysis, prediction and classification of time series data. Ambient intelligence and persuasive computing. Using software tools for big data storage and processing. Case studies: human activity recognition, environmental monitoring, early warning systems for natural disasters, industrial IoT systems, etc.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, практични вежби, тимска работа, пример случаи, поканети предавачи, самостојна изработка на проектна задача и семинарска работа и електронско учење.

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

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

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

45 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

15 hours

16.

Other forms of activities

16.1.

Project tasks

50 hours

16.2.

Independent tasks

30 hours

16.3.

Homework

40 hours

17.

Grading method

17.1.

Tests

45 points

17.2.

Seminar work / 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.

Condition for signature and taking final exam

Реализирани активности 15 и 16

20.

Language of instruction

Македонски или англиски

21.

Quality assurance method

Механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature