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19E034ADO - Algorithms for dynamical optimization

Course specification
Course title Algorithms for dynamical optimization
Acronym 19E034ADO
Study programme Electrical Engineering and Computing
Module
Type of study bachelor academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition no prerequisite
The goal Introduction to the basic dynamic optimization algorithms and their applications in information theory and telecommunications, as well as in other similar scientific fields, like machine learning and bioinformatics.
The outcome Students will learn the basic concepts of statistical decision-making by using iterative algorithms for dynamic optimization. They will also learn how to implement the described graphical models and algorithms and use them to solve various problems related to information transmission and processing.
Contents
Contents of lectures ML detection, Viterbi and Baum-Welch algorithm. MAP detection, BCJR algorithm and its applications in turbo decoding and equalization. Markov, neural and Bayesian networks. Modeling and factor graph based decomposition of optimization problems in engineering. Iterative learning on trees and graphs. Low density parity check codes with representations on graphs. Belief propagation algorithm.
Contents of exercises Software-based demonstrations of iterative dynamic optimization algorithms. Examples of practically significant optimization problems in information theory and related engineering fields. Homeworks that follow lecture topics.
Literature
  1. D. J. C. MecKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press, 2003. (Original title)
  2. T. Richardson, R. Urbanke, Modern Coding Theory, Cambridge University Press, 2009. (Original title)
  3. D. Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press, 2012. (Original title)
  4. T. Cormen, C. Leiserson, R. Rivest, C. Stein, Introduction to Algorithms, 2nd edition, The MIT Press, 2001. (Original title)
  5. P. Ivanis, D. Drajic, Information Theory and Coding - Solved Problems, Springer, New York, 2017. (Original title)
Number of hours per week during the semester/trimester/year
Lectures Exercises OTC Study and Research Other classes
2 2 1
Methods of teaching Teaching methods comprise lectures and precepts. Homeworks and student projects.
Knowledge score (maximum points 100)
Pre obligations Points Final exam Points
Activites during lectures 0 Test paper 60
Practical lessons 40 Oral examination 0
Projects 0
Colloquia 0
Seminars 0