Faculty of Technical Sciences

Subject: Distributed optimization for big data with privacy aware learning (17.DE424)

General information:
 
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
Angelia Nedic, Asu Ozdaglar<\eng> Cooperative Distributed Multi-Agent Optimization<\eng>, poglavlje u knjizi Convex Optimization in Signal Processing and Communications, Y. Eldar, D. Palomar (Eds.)<\eng> 2010 Cambridge University Press<\eng> English
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein Distributed Optimization and Statistical Learning via the Alternating Direction Method of Multipliers 2011 Foundations and Trends in Machine Learning, 3(1):1–122 English
Dimitri P. Bertsekas Nonlinear Programming 2016 Athena Scientific; 3rd edition English
Dimitri P. Bertsekas, John N. Tsitsikli<\eng> Parallel and Distributed Computation: Numerical Methods<\eng> 1989 Prentice Hall<\eng> 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

Assoc. Prof. Bajović Dragana

Associate Professor

Lectures

Faculty of Technical Sciences

© 2024. Faculty of Technical Sciences.

Contact:

Address: Trg Dositeja Obradovića 6, 21102 Novi Sad

Phone:  (+381) 21 450 810
(+381) 21 6350 413

Fax : (+381) 21 458 133
Emejl: ftndean@uns.ac.rs

© 2024. Faculty of Technical Sciences.