OPTIMIZATION MODELS IN TELECOMMUNICATIONS  (TEL 620E)

Classroom, hours:

2419, on Friday's 13:30-16:30

Instructor:

M. Ertuğrul Çelebi,  Professor

Room:  2420

Phone: (212) 285 3558

E-Mail: mecelebi@itu.edu.tr

Url: https://web.itu.edu.tr/mecelebi/

Office Hours: 14:30-16:00 on Wednesday's by appointment.

Course Objective:

To obtain basic concepts of optimization methods, machine learning, with applications to

telecommunications and signal processing.

Content:

Introduction, review of linear algebra and multivariable calculus,

unconstrained and constrained optimization, review of probability

and stochastic processes, parameter estimation, linear mean-square

estimation, stochastic gradient descent, least-squares analysis,

classification, convex optimization, compressed sensing,

kernel methods, Bayesian learning, sampling methods, neural networks

and deep learning.

Grading Policy:

Midterm Exam %40,  Homework %20, term project %40.

The term project will consist of a machine learning application in telecommunications or signal processing. 

It will be based on published works of very recent years.

Textbooks:

-Machine Learning: A Bayesian and Optimization Perspective, 2nd Ed.  Sergios Theodoridis,

Academic Press, 2020

-An Introduction to Optimization, 4th Ed., Edwin K.P. Chong, Stanislaw H. Zak, Wiley, 2013

Useful Books:

-Convex Optimization, Stephen Boyd, L. Wandenberghe, Cambridge U. Press, 2004
Available for download at www.stanford.edu/~boyd/cvxbook/

-Linear and Nonlinear Programming, 3rd Ed., David G. Luenberger, Yinyu Ye, Springer, 2008

-Pattern Recognition and Machine Learning, Christopher M. Bishop, Springer 2006
-Neural Networks and Learning Machines, Simon Haykin, Pearson Education 2009
-Bayesian Reasoning and Machine Learning, David Barber, Cambridge University Press 2012
-Deep Learning, Ian Goodfellow, Yoshua Bengio and Aaron Courville,
 MIT Press 2016, Book link: https://www.deeplearningbook.org/

-Linear Algebra and Optimization for Machine Learning, Charu C. Aggarwal, Springer, 2020

-Machine Learning for FutureWireless Communications, Fa Long Luo Ed., Wiley, 2020

-Applications of Machine Learning in Wireless Communications,  Ruisi He and Zhiguo Ding Eds,

IET 2019

Prerequisites:

Working knowledge of linear algebra, multivariable calculus, probability and MATLAB.