The use of artificial neural networks WF-R-PS-SZS
1. Introduction to the Data Miner package of the Statistica program
2. Construction and interpretation of a regressive artificial neural network solution
3. Construction and interpretation of the neural network classification solution
4. Combining text mining algorithms with artificial neural networks
5. Combining in the analysis of systems of structural equations and artificial neural networks
6. Artificial neural networks and other predictive models
7. Artificial neural networks and other predictive models
(in Polish) E-Learning
(in Polish) Grupa przedmiotów ogólnouczenianych
Subject level
Learning outcome code/codes
Type of subject
Course coordinators
Learning outcomes
Knowledge - the student has basic knowledge of artificial neural networks, their structure, learning methods, types.
Competences and skills - the student is able to count and interpret the results of artificial neural networks.
ECTS:
participation in classes - 15 hours
preparation for classes - 15 hours
NUMBER OF ECTS - 1
Assessment criteria
report on data analysis using artificial neural networks
Bibliography
Elder, J., Hill, T., Miner, G., Nisbet, B., Delen, D., & Fast, A. (2012). Practical Text Mining and Statistical Analysis for Nono-structured Text Data Application. Oxford: Elsevier.
Nisbet, R., Elder, J., & Miner, G. (2009). Handbook of statistical analysis and data mining applications. Burlington, MA: Academic Press (Elsevier).
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: