Courses – Fakultet tehničkih nauka u Novom Sadu

Subject: Advanced Computational Intelligence (19.SEM019)

Native organizations units: No data

Type of studies	Title
Master Academic Studies	Information Engineering (Godina: 1, Summer)
Master Academic Studies	Computing and Control Engineering (Godina: 1, Summer)
Master Academic Studies	Veštačka inteligencija i mašinsko učenje (Godina: 1, Summer)
Master Academic Studies	Software Engineering and Information Technologies (Godina: 1, Summer)

General information:

Category	Theoretical-methodological
Scientific or art field	Applied Computer Science and Informatics
Interdisciplinary	No
ECTS	6

Educational goal:

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

Educational outcome:

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

Course content:

Supervised Learning and Imitation. Training intelligent agents with deep reinforcement learning (deep Q-learning, policy gradients, A3C, etc.). Model-Based Reinforcement Learning. Advanced topics in natural language processing (information extraction, topic modeling, etc.). Advanced topics in recommender systems (content-based, collaborative filtering, discovering latent dependencies, etc.). Advanced topics in graph analysis (clustering, classification, mining interesting patterns). Advanced topics in semi-supervised learning.

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
Maxim Lapan	Deep Reinforcement Learning Hands-On: Apply modern RL methods, with deep Q-networks, value iteration, policy gradients, TRPO, AlphaGo Zero and more	2018	Packt Publishing	English
Ronen Feldman, James Sanger	The Text Mining Handbook: Advanced Approaches in Analyzing Unstructured Data	2006	Cambridge University Press	English
Jure Leskovec, Anand Rajaraman, Jeffrey David Ullman	Mining of Massive Datasets	2014	Cambridge University Press	English