Type of studies | Title |
---|---|
Doctoral Academic Studies | Power, Electronic and Telecommunication Engineering (Year: 2, Semester: Winter) |
Category | Scientific-professional |
Scientific or art field | Telecommunications and Signal Processing |
ECTS | 10 |
The goal of the course is to introduce students to basic principles of distributed optimization, state-of-the art algorithms in this area and their applications, and privacy aware machine learning. Through course project, students will have the opportunity to conduct scientific research in a targeted area of distributed optimization, relevant for student's future PhD thesis.
After successful completion of the course, students will be able to apply the covered algorithms on given optimization, i.e., machine learning problems, and thereby solve them in distributed fashion without compromising data privacy.
- basic principles of distributed optimization - gradient and subgradient method - optimal first order methods - dual decomposition, alternating direction method of multipliers (ADMM) - second order methods: Newton and approximate Newton - stochastic optimization, stochastic approximation - sampling methods - privacy aware learning: local and differential privacy
lectures, consultations, student's independent scientific research, course project
Authors | Title | Year | Publisher | Language |
---|---|---|---|---|
2010 | English | |||
2011 | English | |||
2016 | English | |||
1989 | English |
Course activity | Pre-examination | Obligations | Number of points |
---|---|---|---|
Written part of the exam - tasks and theory | No | Yes | 50.00 |
Project | Yes | Yes | 50.00 |
Associate Professor
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© 2024. Faculty of Technical Sciences.