Smart mobile applications

Smart mobile applications

1.

Subject title

Smart mobile applications

Интелигентни мобилни апликации

2.

Code

m23_s_035

3.

Study program

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

ворн. проф. д-р Ефтим Здравевски ворн. проф. д-р Петре Ламески

9.

Prerequisites for enrollment

10.

Subject goals and competencies:


Within this course, students will get acquainted with the possibilities of design and development of intelligent applications on mobile devices, using existing libraries. Students will be capable of integrating machine learning algorithms and artificial intelligence with mobile applications on different platforms.

11.

Subject content:


Operating systems that support the development of intelligent mobile applications. Overview of algorithms and libraries for intelligent mobile applications. Developing an intelligent mobile application for one of the popular platforms. Integration of models trained with other platforms with the mobile application. Challenges in the development of intelligent mobile applications. Case studies.

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 + 45 + 45 + 30 = 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

45 hours

16.2.

Independent tasks

45 hours

16.3.

Homework

30 hours

17.

Grading method

17.1.

Tests

15 points

17.2.

Seminar work / project (presentation: written and oral)

45 points

17.3.

Activities and learning

15 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

20.

Language of instruction

Македонски и англиски

21.

Quality assurance method

механизам на интерна евалуација и анкети

22.

Literature

22.1.

Mandatory literature

No.

Author

Title

Publisher

Year

6860

Pete Warden

Building Mobile Applications with TensorFlow

O`Reilly

2017

6861

Alexis Perrier

Effective Amazon Machine Learning

Packt

2017

6862

Aurélien Géron

Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

O`Reilly

2017

6863

Sumit Mund

Microsoft Azure Machine Learning

Packt

2015

22.2.

Additional literature

No.

Author

Title

Publisher

Year