Faculty of Technical Sciences

Subject: Selected Chapters in Machine Learning (17.DRNI14)

Native organizations units: Sub-department for Applied Computer Science and Informatics
General information:
 
Category Scientific-professional
Scientific or art field Applied Computer Science and Informatics
ECTS 10

The objective of the course is the acquisition of knowledge in the field of machine learning and understanding the possibilities of implementation of machine learning techniques and methods in different fields.

The student is able to develop new techniques and methods of machine learning and to implement the existing methods in different fields in a new and creative way.

Selected methods and techniques of machine learning. Selected problems whose solution requires the implementation of methods and techniques of machine learning. Examples of solved and unsolved problems. A part of the course work is conducted through independent individual research study work in the field of machine learning. The research study work requires the student`s active and constant interest in and reading of the primary scientific resources and, optionally, writing a paper in the field of machine learning.

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.

Authors Title Year Publisher Language
S. Shalev-Schwartz, S. BenDavid Understanding Machine Learning: From Theory to Algorithms 2014 Cambridge university press English
Goodfellow, I., Bengio, Y., Courville, A. Deep Learning 2017 MIT Press, Cambridge English
M. Magdon-Ismail, Y. AbuMostafa Learning from Data 2012 AMLBook English
Bishop, C.M. Pattern Recognition and Machine Learning 2006 Springer, New York English
Course activity Pre-examination Obligations Number of points
Term paper Yes Yes 50.00
Oral part of the exam No Yes 50.00

Prof. Kovačević Aleksandar

Full Professor

Lectures

Assoc. Prof. Slivka Jelena

Associate Professor

Lectures
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Prof. Kupusinac Aleksandar

Full Professor

Lectures

Faculty of Technical Sciences

© 2024. Faculty of Technical Sciences.

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© 2024. Faculty of Technical Sciences.