Faculty of Technical Sciences

Subject: Computational Intelligence Fundamentals (17.E236A)

Native organizations units: Sub-department for Applied Computer Science and Informatics
General information:
 
Category Professional-applicative
Scientific or art field Applied Computer Science and Informatics
Interdisciplinary No
ECTS 8
Educational goal:

Students gain basic knowledge about the basic principles and techniques of computational (artificial) intelligence.

Educational outcome:

Understanding basic principles and techniques of computational intelligence and the ability to apply them in solving different types of problems.

Course content:

Concepts, aims, techniques, environments, and areas of computational intelligence. Uniformed and informed search techniques applied to problems with or without adversaries. Stochastic environment modeling (Markov Decision Processes). Training intelligent agents with reinforcement learning. Basic principles of machine learning: supervised, unsupervised and semi-supervised learning; basic clustering and classification algorithms. Introduction to neural networks. Introduction to deep learning: convolutional and recurrent neural networks. Introduction to deep reinforcement learning. Introduction to genetic algorithms. Introduction to logic programming in Prolog.

Teaching methods:

Teaching methods include lectures, laboratory classes, homework assignments, and consultations. Lectures involve presenting the course materials using the necessary didactic tools while encouraging the students to participate actively. Laboratory classes (exercises) are realized through assignments that can be done independently or with the help of teaching assistants, as well as through homework assignments.

Literature:
Authors Title Year Publisher Language
Francois Chollet Deep Learning with Python 2017 Manning Publications English
Goodfellow, I., Bengio, Y., Courville, A. Deep Learning 2017 MIT Press, Cambridge English
Stuart Russel, Peter Norwig Artificial Intelligence: A Modern Approach (3rd Edition) 2009 Pearson English
Knowledge evaluation:
Course activity Pre-examination Obligations Number of points
Test Yes Yes 27.00
Written part of the exam - tasks and theory No Yes 45.00
Test Yes Yes 28.00
Lecturers:

Asistent Tošić Saša

Assistant - Master

Computational classes

Asistent Kovačević Tamara

Assistant - Master

Computational classes

prof. dr Kovačević Aleksandar

Full Professor

Lectures

Asistent Anđelić Branislav

Assistant - Master

Computational classes

Asistent Vujinović Aleksandar

Assistant - Master

Computational classes

Asistent Matković Jelena

Assistant - Master

Computational classes

doc. Luburić Nikola

Assistant Professor

Lectures

Faculty of Technical Sciences

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© 2024. Faculty of Technical Sciences.