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19M034ADO - Iterative algorithms for dynamical optimization

Course specification
Course title Iterative algorithms for dynamical optimization
Acronym 19M034ADO
Study programme Electrical Engineering and Computing
Module
Type of study master academic studies
Lecturer (for classes)
Lecturer/Associate (for practice)
Lecturer/Associate (for OTC)
ESPB 6.0 Status elective
Condition -
The goal Introduction to the basic iterative dynamic optimization algorithms and their aplications in information theory, telecommunications, artificial intelligence and bionformatics.
The outcome Students will learn the basic concepts of iterative algorithms for dynamic optimization. They will also learn how to implement the described algorithms and use them to solve various problems related to information transmition and processing.
Contents
Contents of lectures Modelling and factor graph based decomposition of optimization problems in engineering. Finite state machines, Bayesian and Markov networks. Belief propagation algorithm. Iterative decoding of turbo codes and low-density parity-check codes. Viterbi and Baum-Welch methods, applications in channel equalization. Hidden Markov processes. Iterative learning on graphs. Gradient-based optimizations.
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. T. Hastie, R. Tibshirani, J. Friedman, The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer, 2009 (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