Subject

Intelligent Mobile Applications

Course Title

Intelligent Mobile Applications
Smart mobile applications

Code

m23_s_035

Study Programme

—

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

Eftim Zdravevski, Petre Lameski

Prerequisites for enrolling in the course

—

10.

Objectives of the course programme (competences)

Within this course, students will become familiar with the possibilities for designing and developing intelligent applications on mobile devices using existing libraries. Students will be equipped to integrate machine learning and artificial intelligence algorithms with mobile applications across various platforms.

11.

Course content

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

12.

Learning methods

Lectures supported by slide presentations, interactive lectures, practical classes (using equipment and software packages), teamwork, case studies, guest lecturers, independent preparation and defence of a project assignment and seminar paper, and learning in an electronic environment (forums and consultations).

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

60 + 0 + 45 + 45 + 30 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	60 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	0 hours

16.

Other forms of activities

16.1.	Project assignments	45 hours
16.2.	Independent assignments	45 hours
16.3.	Home study	30 hours

17.

Assessment method

17.1.	Tests	15 points
17.2.	Seminar paper / 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.

Requirement for obtaining a signature and taking the final exam

completed activities

20.

Language of instruction

Macedonian and English

21.

Method for monitoring the quality of teaching

internal evaluation and survey mechanism

22.

Literature

22.1.	Required literature 1. Pete Warden \| Building Mobile Applications with TensorFlow \| O'Reilly \| 2017 2. Alexis Perrier \| Effective Amazon Machine Learning \| Packt \| 2017 3. Aurélien Géron \| Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems \| O'Reilly \| 2017 4. Sumit Mund \| Microsoft Azure Machine Learning \| Packt \| 2015
22.2.	Additional literature No. Author Title Publisher Year