Courses – Fakultet tehničkih nauka u Novom Sadu

Subject: (17.EAI552)

Type of studies	Title
Master Academic Studies	Artificial Intelligence and Machine Learning (Year: 2, Semester: Winter)

General information:

Category	Theoretical-methodological
Scientific or art field	Telecommunications and Signal Processing
ECTS	6

Introducing students with computer and network infrastructure for storage of large amounts of data, great heterogeneity and large arrival speed. Acquiring knowledge and mastering practical skills in apply different algorithms for analyzing and managing large sets of data (Big Data).

Identifying value in data and discovering knowledge from data.Construction of physical and virtual storage for storing large amounts of data. Students will be able to use the distributed file systems and MapReduce as a tool for creating parallel algorithms that succeed on big data. Understanding and implementation of the algorithms for big data management and combining and evaluating algorithms for mining big data sets.

Data warehousing. Distributed file systems (Hadoop, Spark). Virtual warehouses and communications. Database virtualization. Management of large databases on cloud. MapReduce program model for distributed data processing. Data Ssearches (Similar Samples, Frequency Sample Samples). Data in the form of graphs, link analysis, local and global topological attributes. Machine learning algorithms for big data. Data visualization.

Lectures, computer lab sessions (Matlab, Python), homework, consultations, active learning, project and research based learning, students' competitions.

Authors	Title	Year	Publisher	Language
Greg Schulz	Software-Defined Data Infrastructure Essentials: Cloud, Converged, and Virtual Fundamental Server Storage I/O Tradecraft	2017	CRC Press	English
J. Leskovac, A. Rajaraman, J. D. Ullman	Mining of Massive Datasets, 3rd Ed	2020	Cambridge University Press	English
Tam Sel	Apache Spark and Hadoop for beginners	2020	Kindle Direct Publishing	English