FI:P069 Hybrid Systems of Machine Lear - Course Information
P069 Hybrid Systems of Machine Learning
Faculty of InformaticsSpring 2002
- Extent and Intensity
- 2/1. 3 credit(s) (plus extra credits for completion). Recommended Type of Completion: zk (examination). Other types of completion: k (colloquium), z (credit).
- Teacher(s)
- doc. Ing. Jan Žižka, CSc. (lecturer)
- Guaranteed by
- prof. PhDr. Karel Pala, CSc.
Department of Machine Learning and Data Processing – Faculty of Informatics
Contact Person: doc. Ing. Jan Žižka, CSc. - Timetable
- Thu 11:00–12:50 B116, Thu 13:00–14:50 A107, Thu 16:00–16:50 B116
- Course Enrolment Limitations
- The course is also offered to the students of the fields other than those the course is directly associated with.
- fields of study / plans the course is directly associated with
- Informatics (programme FI, B-IN)
- Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-IN)
- Upper Secondary School Teacher Training in Informatics (programme FI, M-SS)
- Information Technology (programme FI, B-IN)
- Course objectives
- This subject introduces basic principles of artificial neural networks and their applications from the practical point of view. It focuses on utilization of combined (hybrid) machine learning methods both for applications and automated support of designing learning algorithms by selected machine learning methods.
- Syllabus
- Artificial perceptrons and neural networks. Basic learning algorithms, delta-rule, back propagation of errors. Features of basic neural network models, problems with over-training and network designs.
- Transformation of decision trees to neural networks, initialization of weigths.
- Genetic algorithms, their combinations with neural networks, optimalization ow weigths.
- Hybrid neural networks, combination of inputs and weights using t-norms and t-conorms. AND and OR fuzzy neuron. Fuzzy-neural networks. ANFIS and NEFCON architectures. Neuro-fuzzy classifiers. Optimalization of fuzzy sets within IF-THEN rules.
- Hebbian learning. Recurrent and Hopfield networks. Other types of networks.
- Application examples.
- Literature
- MITCHELL, Tom M. Machine learning. Boston: McGraw-Hill, 1997, xv, 414. ISBN 0070428077. info
- Assessment methods (in Czech)
- Výuka formou přednášek a cvičení. Zkouška písemná.
- Language of instruction
- Czech
- Enrolment Statistics (recent)
- Permalink: https://is.muni.cz/course/fi/spring2002/P069