Student acquires basic knowledge in concepts and methods for artificial neural networks with applications in pattern recognition and analysis of various types of information such as signals and images, in various application areas. Students gain knowledge on neuron models, learning algorithms, associative networks, Rosenblatt perceptron, multi-layer perceptron, radial-basis function networks, support vector machines, recursive networks, self-organising networks, and genetic algorithms. Students are able to use computer software for neural network simulation to implement neural networks.
- define and describe concepts of artificial neural networks
- list examples of neural network applications
- classify methods for artificial neural networks
- breakdown a practical problem of neural network learning and exploitation
- combine acquired knowledge and propose a solution to the given problem
- evaluate a practical solution obtained using neural networks
Forms of Teaching
Lectures are delivered according to the published plan and on the basis of the materials that are made available to the students.Exams
Student exams are in the form of a mid-term exam and a final exam that are graded together with other student activities. Students who do not pass through continuing evaluation can take exam at a final examination term.Laboratory Work
Laboratory excercises are completed according to the published plan and based on the instructions available to students. Laboratory involves software implementation of various neural networks.Consultations
Oral consultations are available on student request.Other Forms of Group and Self Study
Student team project.
|Type||Threshold||Percent of Grade||Threshold||Percent of Grade|
|Laboratory Exercises||50 %||20 %||50 %||0 %|
|Seminar/Project||50 %||20 %||50 %||0 %|
|Mid Term Exam: Written||50 %||30 %||0 %|
|Final Exam: Written||50 %||30 %|
|Exam: Written||60 %||50 %|
|Exam: Oral||50 %|
Week by Week Schedule
- Introductory course remarks. Introduction to neural networks - part one.
- Introduction to neural networks - part two.
- Learning process. Associative memory.
- Rosenblatt perceptron. LMS algoritam.
- Multilayer perceptron.
- Radial-basis function networks.
- Support vector machine.
- Midterm exam.
- Recursive networks. Hopfield network.
- Self-organizing networks.
- Genetic algorithms and evolutionary strategies.
- Practical applications of neural networks.
- Presentations of student team projects.
- Final exam.