Study program / study programs: Advanced data analytics
Course: Artificial Intelligence / Machine Learning
Teacher(s): Vladan B. Devedžić, Bojan B. Tomić, Zoran V. Ševarac, Dragan O. Đurić, Aleksandra Alorić, Marija Mitrović Dankulov, Andrej Korenić, Sonja Dimitrijević
Course status: Elective
ECTS points: 10
Prerequisites:
Course objective:
Mastering the fundamentals, techniques and applications of artificial intelligence.
Learning outcomes:
Students will learn basic concepts and techniques of artificial intelligence and gain practical skills for their application in advanced data analysis.
Course structure and content:
Lectures
Basic concepts and overview of the domain of Artificial Intelligence and Intelligent Systems. Basics of Machine Learning. Methods and techniques of data preparation and attribute selection. Algorithms for linear regression, classification and clustering.
Rule-based knowledge representation. Rule-based decision making.
Basic concepts of neural networks.
Labs
Work on practical problems using software frameworks, tools and/or services specific to each of the areas covered in this course. The software frameworks that students will work with are based on Java, Python and/or R programming languages.
Literature/Readings:
S. Russell, P. Norvig, Artificial Intelligence – A Modern Approach, The 3rd Edition. Prentice Hall, Englewood Cliffs, NJ, 2009.
Online materials hosted on the course website
Additional literature: Documentation and tutorials for software frameworks, tools and services that are used during the labs.
The number of class hours per week:
Lectures: 4
Labs: 0
Workshops: 1
Research study: 2
Other classes: 0
Teaching methods:
Lectures: slides and case studies related to the covered concepts and technologies.
Other: practical work with students on a computer covering real-world use cases, students are actively involved in the discussion.
Evaluation/Grading (maximum 100 points):
Pre-exam requirements (Simple project): 30
Final exam (Project): 70