Courses – Fakultet tehničkih nauka u Novom Sadu

Subject: Machine Learning (17.DE120)

Type of studies	Title
Doctoral Academic Studies	Power, Electronic and Telecommunication Engineering (Year: 1, Semester: Winter)
Doctoral Academic Studies	Mathematics in Engineering (Year: 2, Semester: Winter)
Doctoral Academic Studies	Mathematics in Engineering (Year: 1, Semester: Winter)

General information:

Category	Scientific-professional
Scientific or art field	Electronics
ECTS	10

The main goal of this course is to introduce the students to the basics, as well as some advanced approaches, trends and tools in the field of machine learnings systems design.

Students who successfully complete this course should be able to follow the latest results, understand the latest technical and scientific literature and get involved into research work in this area. Beside theoretical knowledge, students will also gain experience in using contemporary design tools used to develop machine learning systems.

Introduction to machine learning. Overview of classical machine learning models (Support Vector Machines, Decision Trees. Artificial Neural Networks). Deep learning. Regularization techniques for deep learning. Optimization techniques for deep learning. Convolutional Neural Networks. Recurrent and Recursive Networks. Neural Architecture Search techniques. Autoencoders. Deep Generative models. Deep Reinforcement learning.

Lectures will be performed on an individual basis with each student. Teacher will, in cooperation with each student, select his/her's areas of interest and propose a scientific literature and topic that a student should prepare and present.