The use of artificial neural networks

General data

Course ID:	WF-R-PS-SZS
Erasmus code / ISCED:	14.41 Kod klasyfikacyjny przedmiotu składa się z trzech do pięciu cyfr, przy czym trzy pierwsze oznaczają klasyfikację dziedziny wg. Listy kodów dziedzin obowiązującej w programie Socrates/Erasmus, czwarta (dotąd na ogół 0) – ewentualne uszczegółowienie informacji o dyscyplinie, piąta – stopień zaawansowania przedmiotu ustalony na podstawie roku studiów, dla którego przedmiot jest przeznaczony. / (0313) Psychology The ISCED (International Standard Classification of Education) code has been designed by UNESCO.
Course title:	The use of artificial neural networks
Name in Polish:	S: Zastosowanie sztucznych sieci neuronowych
Organizational unit:	Institute of Psychology
Course groups:	(in Polish) Grupa przedmiotów ogólnouczelnianych - Doktoranci (in Polish) Przedmioty dla doktorantów psychologii (in Polish) Seminaria tematyczne z psychologii
ECTS credit allocation (and other scores):	(not available) Basic information on ECTS credits allocation principles: the annual hourly workload of the student’s work required to achieve the expected learning outcomes for a given stage is 1500-1800h, corresponding to 60 ECTS; the student’s weekly hourly workload is 45 h; 1 ECTS point corresponds to 25-30 hours of student work needed to achieve the assumed learning outcomes; weekly student workload necessary to achieve the assumed learning outcomes allows to obtain 1.5 ECTS; work required to pass the course, which has been assigned 3 ECTS, constitutes 10% of the semester student load. view allocation of credits
Language:	Polish
Subject level:	intermediate
Learning outcome code/codes:	SD_ PS _W01 SD_ PS _W03 SD_ PS _U02 SD_ PS _U03 SD_ PS _K02 P8S_WG; P8S_WK
Short description:	The course is devoted to basic information on artificial neural networks. Students learn about the types of networks, how networks learn and how they make predictions. The course deals with the validity of the network and comparing its solution with other predictive models. By doing the exercises, students learn to make analyzes using various types of artificial neural networks.
Full description:	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
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).
Efekty kształcenia i opis ECTS:	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 methods and assessment criteria:	report on data analysis using artificial neural networks

This course is not currently offered.

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