Discovering knowledge from environmental data

1.

Subject title

Discovering knowledge from environmental data

Откривање на знаење од податоци за животната средина

2.

Code

EI-Z-04

3.

Study program

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

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 Андреја Наумоски проф. д-р Коста Митрески

9.

Prerequisites for enrollment

10.

Subject goals and competencies:

Getting to know the principles of disclosure of knowledge in data from enviroment.

11.

Subject content:

1) Introduction to detection of knowledge in environmental data. 2) Basic knowledge and ability to analyze data using methods of Machine learning. 3) Use of these methods for analyzing environmental data. 4) As part of the practical work, they will be trained for useless use to some of the mechanical methods of detecting knowledge of life data environment. 5) Introduction to Detection of Knowledge and Methods of Machine Learning Decision -making stems and regression stems - learning the rules. Classification with probability, method of closest neighbor, discovering equations. 6) Examples of machine learning applications in data analysis enviroment Biological classification of rivers (example: rivers from Slovenia and Macedonia, biodegradability prediction.) Modeling the population dynamics and the habitat lives of The bear, lynx and others. 7) Practical work with data obtained from measurements, using different Machine learning methods.

12.

Learning methods:

Предавања поддржани со презентации преку слајдови, интерактивни предавања, вежби (користење на опрема и софтверски пакети), тимска работа, пример случаи, поканети гости предавачи, самостојна изработка и одбрана на проектна задача и семинарска работа, учење во електронско опкружување (форуми, консултации).

13.

Total available time fund

6.0 ECTS x 30 hours = 180 hours

14.

Time distribution

60 + 0 + 40 + 60 + 20 = 180 hours

15.

Forms of teaching activities

15.1.

Lectures - theoretical teaching

60 hours

15.2.

Exercises (laboratory, classroom), seminars, team work

0 hours

16.

Other forms of activities

16.1.

Project tasks

60 hours

16.2.

Independent tasks

40 hours

16.3.

Homework

20 hours

17.

Grading method

17.1.

Tests

45 points

17.2.

Seminar work / project (presentation: written and oral)

60 points

17.3.

Activities and learning

10 points

17.4.

Final exam

100 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, 16

20.

Language of instruction

македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

22.2.

Additional literature