Learning Automata
Data is displayed for academic year: 2023./2024.
Lecturers
Course Description
Fundamentals and definitions of machine learning. State and action probabilities, and standard patterns of machine learning behaviour. Mathematical models of neuron and neural networks, diversity and properties of learning alorithms. Claster analisys, knowledge representation, compression and representation from uncomplete data. Basics of associative memories. Recursive networks and processing of time dependent patterns. Learning algorithms: Back-propagation with extensions, Kohonen and Cascade correlation algorithm, Convolutional networks, Deep learning, WTA (Winner-Takes-All). Feature extraction by competitive learning - interactive or on-line learning, Sarsa and Q-learning algorithm, Markov models. Intelligent agents and selfadaptive control of communication services.
Study Programmes
University graduate
[FER3-HR] Audio Technologies and Electroacoustics - profile
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[FER3-HR] Communication and Space Technologies - profile
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[FER3-HR] Computational Modelling in Engineering - profile
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[FER3-HR] Computer Engineering - profile
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[FER3-HR] Computer Science - profile
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[FER3-HR] Control Systems and Robotics - profile
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[FER3-HR] Data Science - profile
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[FER3-HR] Electrical Power Engineering - profile
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[FER3-HR] Electric Machines, Drives and Automation - profile
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[FER3-HR] Electronic and Computer Engineering - profile
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[FER3-HR] Electronics - profile
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[FER3-HR] Information and Communication Engineering - profile
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[FER3-HR] Network Science - profile
Elective Courses
(1. semester)
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Elective Courses of the Profile
(1. semester)
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[FER3-HR] Software Engineering and Information Systems - profile
Elective Courses
(1. semester)
(3. semester)
[FER2-HR] Telecommunication and Informatics - profile
Specialization Course
(1. semester)
(3. semester)
Learning Outcomes
- to define concept, methods and architectures typical for machine learnings
- to explain how machine learning operate and basic purpose
- to apply knowledge about machine learning to communication services
- to analyze functions of machine learned components, as well as their interactions in order to find appropriate solution
- to analyze organization and results produced by machine learning modela
- to create machine learned models including various types of recognition and self-adaptation
- to evaluate and assess solutions based on different methods of machine learning
Forms of Teaching
Lectures
Independent assignments
Laboratory
Independent assignments
Laboratory
Grading Method
Continuous Assessment | Exam | |||||
---|---|---|---|---|---|---|
Type | Threshold | Percent of Grade | Threshold | Percent of Grade | ||
Laboratory Exercises | 0 % | 20 % | 0 % | 0 % | ||
Homeworks | 0 % | 10 % | 0 % | 0 % | ||
Class participation | 0 % | 10 % | 0 % | 0 % | ||
2. Mid Term Exam: Written | 0 % | 20 % | 0 % | |||
Final Exam: Written | 0 % | 20 % | ||||
Final Exam: Oral | 20 % | |||||
Exam: Written | 40 % | 0 % | ||||
Exam: Oral | 30 % |
Week by Week Schedule
- Perceptron (learning paradigms,Hebbian learning, competitive learning, Boltzmann learning)
- Multilayer perceptron (error-backpropagation learning, credit-assignment problem, backpropagation through time)
- Multilayer perceptron (error-backpropagation learning, credit-assignment problem, backpropagation through time)
- Radial basis function networks (solving interpolation problem with radial basis function networks, generalized radial basis function networks, relation to regularization theory)
- Self-organizing networks (Hebbian non-supervised learning, Oja's learning rule, PCA using self-organizing network, Sanger's learning rule, Competitive non-supervised learning, winner-takes-all network, Kohonen's self-organizing maps)
- Recurrent neural networks (Hopfield network, Hopfield network energy function, Boltzman machine, Elman networks, Jordan networks) and learning algorithms (back propagation through time, reccurent backpropagation)
- Online learning
- Midterm exam
- Online learning
- Deep convolutional networks: layers, architectures, visualization, fine tuning, applications, implementation
- Deep convolutional networks: layers, architectures, visualization, fine tuning, applications, implementation
- Decision trees (ID3, C4;5)
- K-means algorithm
- k-Nearest Neighbour, Max-min clustering
- Final exam
Literature
(.), Neural Networks and Learning Machines, Simon Haykin, Prentice Hall, c2009, New York,
(.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition David E. Rumelhart MIT Press 1987.,
(.), Reinforcement Learning:An Introduction Richard S. Sutton, Andrew G. Barto MIT Press, Cambridge 1998.,
For students
General
ID 222473
Winter semester
5 ECTS
L1 English Level
L1 e-Learning
30 Lectures
0 Seminar
0 Exercises
15 Laboratory exercises
0 Project laboratory
0 Physical education excercises
Grading System
85 Excellent
70 Very Good
60 Good
50 Sufficient