Subject

Intelligent systems

Course Title

Intelligent systems
Intelligent Systems

Code

F23L3S107

Study Programme

Bioinformatics

Organizer of the study programme (unit, institute, department or division)

Faculty of Computer Science and Engineering

Degree level (first, second, third cycle)

First Cycle

Academic year / semester

8 / Summer

Number of ECTS credits

Teacher

Ana Madevska Bogdanova

Prerequisites for enrolling in the course

Artificial Intelligence or Introduction to Data Science or Machine Learning

10.

Objectives of the course programme (competences)

The goal of the course is to round out students' knowledge in the field of intelligent systems, from data preprocessing to the validation of the built system. Students will be equipped to build an intelligent system from start to finish for real-world problems in a specific domain, using tools to automate the IS development process.

11.

Course content

Lectures:
1. Introduction to the subject matter
2. Overview of the domains in which modern Intelligent Systems are used (medicine, language processing, robotics)
3. Modern data preprocessing techniques - Data Engineering: data selection, data cleaning, feature engineering
4. Modern machine learning and deep learning techniques for building IS models; Transfer learning;
5. Modern methods for evaluation of IS models (classification and regression);
6. Discriminative versus generative methods for building IS;
7. Interpretation of built models - Shapley value (SHAP);
8. How to Avoid Hidden Problems and Hazards When Building Intelligent Systems.
9. Handling a Real-World Problem – Methods for Optimal Selection of Preprocessing Techniques
10. Real-world problem processing - model building, evaluation
11. Interpretation of the built models
12. Automation of the intelligent system development process - end-to-end lifecycle

Practical Classes:
1. Introduction to Intelligent Systems
2. Overview of the domains where modern Intelligent Systems are used - examples
3. Modern Data Preprocessing Techniques - Examples in Python
4. Modern Machine Learning and Deep Learning Techniques for Building IS Models - Examples in Python
5. Modern Methods for Evaluating IS Models (Classification and Regression) - Examples in Python
6. Discriminative vs. Generative Methods for Building AI - Examples in Python
7. SHAP in Python, Weighted SHA, SHAP with GPU
8. How to Avoid Hidden Problems and Hazards When Building Intelligent Systems.
9. Preprocessing biosignals with Python tools using patient datasets
10. Building Predictive Models with Python Tools
11. Interpretation of the built models
12. Tools for AI model building automation (open source platform - MLflow)

12.

Learning methods

Lectures using presentations, interactive lectures, exercises (using equipment and software packages), teamwork, case studies, guest lectures, independent preparation and defense of a project assignment and a seminar paper.

13.

Total available time

6 ECTS x 30 hours = 180 hours

14.

Distribution of available time

30 + 45 + 15 + 15 + 75 = 180 hours

15.

Forms of teaching activities

15.1.	Lectures - theoretical instruction	30 hours
15.2.	Exercises (laboratory, auditory), seminars, teamwork	45 hours

16.

Other forms of activities

16.1.	Project assignments	15 hours
16.2.	Independent assignments	15 hours
16.3.	Home study	75 hours

17.

Assessment method

17.1.	Tests	0 points
17.2.	Seminar paper / project (presentation: written and oral)	15 points
17.3.	Activities and learning	10 points
17.4.	Final exam	20 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 15.2 and 16.1 have been completed.

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. Geoff Hulten \| Building Intelligent Systems: A Guide to Machine Learning Engineering \| APRESS \| 2018 2. Kavita Taneja, Harmunish Taneja, Kuldeep Kumar, Arvind Selwal, Eng Lieh Ouh \| Data Science and Innovations for Intelligent Systems \| Taylor&Francis \| 2022
22.2.	Additional literature No. Author Title Publisher Year