Courses – Fakultet tehničkih nauka u Novom Sadu

Subject: Artificial Intelligence in Computer Animations (17.IA024)

Native organizations units: No data

Type of studies	Title
Master Academic Studies	Engineering Animation (Godina: 1, Summer)

General information:

Category	Theoretical-methodological
Scientific or art field	Design
Interdisciplinary	Yes
ECTS	4

Educational goal:

Obtaining basic knowledge in artificial intelligence and machine learning for practical application to computer vision and computer graphics tasks. Automation and optimization of man-machine cognitive interactivity in order to improve solution efficiency. Problem solving optimization of complex and sophisticated tasks in computer graphics and animations in order to reach high level of autonomy in different applications.

Educational outcome:

The knowledge of basic techniques for artificial intelligence and machine learning and their application for automatic solving of problems in computer vision and computer graphics.

Course content:

Artificial intelligence techniques in 3D scene modelling and rendering. Intelligent techniques for automatic behaviour modelling and animations. Intelligent techniques for visualization, reasoning and interaction. Machine learning for automation of the statistical analysis of large complex datasets by adaptive computing. Machine learning application for various computer graphics and computer vision problems to reach high level of solution autonomy. Graphical models and inferences. Classification methods and neural networks. Probability reasoning through inference. Path Finding algorithms and fuzzy systems. Advanced methods for automatic decision making.

Teaching methods:

Computer practice is based on mastering and understanding basic concepts and techniques in artificial intelligence through practical applications in computer vision and computer graphics problems. Computer practice will be performed using C++ programming language with supporting libraries for artificial intelligence, machine learning, computer vision and computer graphics. This includes OpenCV, OpenGL, MLC++, LifeAI, Boost, OpenAI, FANN, Ogre3D and other necessary open source libraries. Two subject assignments and one final project are foreseen as pre-exam obligations. Each subject assignment can produce maximally 15% of total points while final project can carry maximally 30% of total points. Student must collect minimum of 30% of points from the pre-exam obligatory tasks in order to be able to take the final theory exam. Final grade of the subject is formed based on teaching and exercise class attendance, collected points on pre-exam tasks and final theory exam success.

Literature:

Authors	Title	Year	Publisher	Language
N. Sebe, I. Cohen, A. Garg, T. S. Huang	Machine Learning in Computer Vision	2005	Springer	English
D. Plemenos, G. Miaoulis	Artificial Intelligence Techniques for Computer Graphics	2008	Studies in Computational Intelligence, Volume 159, Springer	English
Bishop, C.M.	Pattern Recognition and Machine Learning	2006	Springer, New York	English
-	Veštačka inteligencija	2014	Skripta	Serbian language
S. Russell, P. Norvig	Artificial Intelligence: A Modern Approach	2007	Pearson Education Limited	English

Knowledge evaluation:

Course activity	Pre-examination	Obligations	Number of points
Project task	Yes	Yes	15.00
Project	Yes	Yes	30.00
Project task	Yes	Yes	15.00
Oral part of the exam	No	Yes	30.00
Lecture attendance	Yes	Yes	5.00
Exercise attendance	Yes	Yes	5.00