Artificial neural networks 1100-3BN22
Sylabus:
1. Linear regression
2. Linear neural networks
3. Perceptron
4. Backpropagation algorithm
5. Classification and logistic regression
6. Generative algorithms
7. Support Vector Machines
8. Unsuppervised learning
9. Reinforced learning.
Theoretical concepts presented during lectures will be illustrated with practical examples in python during the hands-on classes.
Mode
Prerequisites (description)
Learning outcomes
Knowledge:
Student knows the basic concepts of machine learning and the artificial neural networks
Skills:
Student can apply machine lrearning techniques to practical problems.
Attitudes:
1 recognizes the importance of machine learning methods in modern data analysis
2 appreciates the work in deepening their knowledge and skills in the area of machine learning
Assessment criteria
The mark is an average of the result of the theoretical test and the solution of a practical problem.
Practical placement
None
Bibliography
1. R. Tadeusiewicz, Sieci neuronowe.
2. Timothy Masters, Sieci neuronowe w praktyce Programowanie w języku C++.
3. J.Hertz, A. Krogh, R. Palmer, Wstęp do teorii obliczeń neuronowych.
4. S. Osowski, Sieci neuronowe w ujęciu algorytmicznym.
5. Z. Świątnicki R. Wantoch-Rekowski, Sieci neuronowe w zastosowaniach wojskowych.
6. J. Korbicz, A. Obuchowicz, D. Uciński, Sztuczne sieci neuronowe - podstawy i zastosowania.
7. D. Rutkowska, M. Piliński, L. Rytkowski, Sieci neuronowe, algorytmy genetyczne i systemy rozmyte.
8. J. Chromiec, E. Strzemieczna, Sztuczna inteligencja - Metody konstrukcji i analizy systemów eksperckich.
9. J.J. Mulawka, Systemy ekspertowe.
10. Roman Wantoch-Rekowski , Sieci neuronowe w zadaniach-perceptron wielowarstwowy
11. Russel Norvig, Artificial intelligence a modern approach.
Additional information
Information on level of this course, year of study and semester when the course unit is delivered, types and amount of class hours - can be found in course structure diagrams of apropriate study programmes. This course is related to the following study programmes:
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: