Course detail
Neural Networks and Evolution Methods
FSI-VSCAcad. year: 2019/2020
The course introduces basic approaches to Soft Computing and classical methods used in the field. Practical use of the methods is demonstrated on solving simple engineering problems.
Language of instruction
Number of ECTS credits
Mode of study
Guarantor
Learning outcomes of the course unit
Prerequisites
Co-requisites
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Course curriculum
Work placements
Aims
Specification of controlled education, way of implementation and compensation for absences
Recommended optional programme components
Prerequisites and corequisites
Basic literature
Recommended reading
Elearning
Classification of course in study plans
Type of course unit
Lecture
Teacher / Lecturer
Syllabus
2. Evolutionary algorithms I. (genetic algorithms, evolutionary strategies, differential evolution).
3. Evolutionary algorithms II. (grammatical evolution, genetic programming).
4. Selected optimization metaheuristics (HC, HC12, THC, simulated annealing).
5. SWARM Intelligence (PSO, ACO, SOMA).
6. Architectures and classification of neural networks. Perceptron.
7. Feedforward neural networks, single and multilayer networks. ADALINE. Back Propagation Algorithm. Optimization methods used in ANN design.
8. RBF and RCE neural networks. Topologically organized neural networks (competitive learning, Kohonen maps).
9. Cluster analysis. Task dimension reduction. Principal component analysis. LVQ neural networks, neural networks ART.
10. Associative neural networks (Hopfield, BAM), behavior, state diagram, attractors, learning. and Neocognitron.
11. Deep Neural Network. CNN. Transfer Learning.
12. Spiking neural Network.
13. Case studies. Deterministic chaos and its control.
Computer-assisted exercise
Teacher / Lecturer
Syllabus
- Implementation of basic metaheuristics
- solving global optimization problems
- use of global optimization toolbox
- use of deep neural network toolbox
- creation of nonlinear models using neural networks
- deep learning in computer vision for image classification
- detection of objects in Image using Deep Learning (R-CNN)
- Semantic Image Segmentation using Deep Learning (SegNet)
- validation of CNN learning and control of learned networks using deep dream method
Elearning