The aim of studying and bibliography

The main aim of study:

The general information about the module:

Teaching materials: Dostępne w formie elektronicznej na stronie: https://mkusy.v.prz.edu.pl/

Bibliography required to complete the module

Bibliography used during lectures

1	Bishop C.M.	Pattern Recognition and Machine Learning	Springer.	2006
2	Tadeusiewicz R.	Sieci neuronowe	Akademicka Oficyna Wydawnicza, Warszawa.	1993
3	Vapnik V.	The nature of statistical learning theory	Springer, New York.	1995
4	Quinlan J.R.	C4.5: Programs for machine learning	Morgan Kaufman Publishers, San Meteo.	1993
5	Mathworks Inc. Matlab Online Documentation http://www.mathworks.com		.	2017

Bibliography used during classes/laboratories/others

1

Mathworks Inc.

Matlab Online Documentation

http://www.mathworks.com.

2019

Bibliography to self-study

1	Gunn S.	Support Vector Machines for Classification and Regression	University of Southampton.	1998
2	Masters T.	Practical Neural Network Recipes in C++	Academic, San Diego.	1993

Basic requirements in category knowledge/skills/social competences

Formal requirements: The student satisfies the formal requirements set out in the study regulations.

Basic requirements in category knowledge:

Basic requirements in category skills:

Basic requirements in category social competences:

Module outcomes

MEK	The student who completed the module	Types of classes / teaching methods leading to achieving a given outcome of teaching	Methods of verifying every mentioned outcome of teaching	Relationships with KEK	Relationships with PRK
01				K_W06+ K_U09+ K_U10+ K_K04+	P6S_KO P6S_KR P6S_UW P6S_WG
02				K_W06+ K_U09+ K_U10+ K_K04+	P6S_KO P6S_KR P6S_UW P6S_WG
03				K_W06+ K_U09+ K_U10+ K_K04+	P6S_KO P6S_KR P6S_UW P6S_WG
04				K_W06+ K_U09+ K_U10+ K_K04+	P6S_KO P6S_KR P6S_UW P6S_WG
05				K_W06+ K_U09+ K_U10+ K_K04+	P6S_KO P6S_KR P6S_UW P6S_WG
06				K_W06+ K_U09+ K_U10+ K_K04+	P6S_KO P6S_KR P6S_UW P6S_WG

Attention: Depending on the epidemic situation, verification of the achieved learning outcomes specified in the study program, in particular credits and examinations at the end of specific classes, can be implemented remotely (real-time meetings).

The syllabus of the module

Sem.	TK	The content	realized in	MEK
5	TK01	-	W01	MEK01
5	TK02	-	W02	MEK02
5	TK03	-	W03	MEK04
5	TK04	-	W04	MEK05
5	TK05	-	W05	MEK05
5	TK06	-	W06	MEK05
5	TK07	-	W07	MEK06
5	TK08	-	W08	MEK06
5	TK09	-	W09	MEK03
5	TK10	-	W10	MEK03

The student's effort

The type of classes	The work before classes	The participation in classes	The work after classes
Lecture (sem. 5)		contact hours: 20.00 hours/sem.
Laboratory (sem. 5)		contact hours: 20.00 hours/sem.
Advice (sem. 5)	The preparation for Advice: 2.00 hours/sem.	The participation in Advice: 1.00 hours/sem.
Credit (sem. 5)	The preparation for a Credit: 7.00 hours/sem.	The written credit: 2.00 hours/sem. The oral credit: 1.00 hours/sem.

The way of giving the component module grades and the final grade

The type of classes	The way of giving the final grade
Lecture
Laboratory
The final grade

Sample problems

Required during the exam/when receiving the credit
(-)

Realized during classes/laboratories/projects
(-)

Others
(-)

Can a student use any teaching aids during the exam/when receiving the credit : no

The contents of the module are associated with the research profile: yes

1	M. Kusy; R. Zajdel	New data reduction algorithms based on the fusion of instance and feature selection	2024
2	A. Konior; P. Kowalski; M. Kusy; M. Szwagrzyk	Machine learning techniques for explaining air pollution prediction	2022
3	J. Izydorczyk; P. Kowalski; M. Kusy; M. Szwagrzyk	Estimation of atmospheric boundary layer values in the context of the daily prediction of PM10 air pollution	2022
4	P. Kowalski; M. Kusy	Algorithms for Triggering General Regression Neural Network	2022
5	P. Kowalski; M. Kusy	Architecture reduction of a probabilistic neural network by merging k-means and k-nearest neighbour algorithms	2022
6	P. Kowalski; M. Kusy	Detection of Fraudulent Credit Card Transactions by Computational Intelligence Models as a Tool in Digital Forensics	2022
7	J. Kielpinska; A. Konior; P. Kowalski; M. Kusy; M. Szwagrzyk	Numerical analysis of factors, pace and intensity of the corona virus (COVID-19) epidemic in Poland	2021
8	M. Kusy; R. Zajdel	A weighted wrapper approach to feature selection	2021
9	J. Kluska; M. Kusy; R. Zajdel; T. Żabiński	Fusion of Feature Selection Methods for Improving Model Accuracy in the Milling Process Data Classification Problem	2020
10	J. Kluska; M. Kusy; R. Zajdel; T. Żabiński	Weighted Feature Selection Method for Improving Decisions in Milling Process Diagnosis	2020
11	P. Kowalski; S. Kubasiak; M. Kusy; S. Łukasik	Probabilistic Neural Network - parameters adjustment in classification task	2020
12	J. Kluska; M. Kusy; B. Obrzut; M. Obrzut; A. Semczuk	Prediction of 10-year Overall Survival in Patients with Operable Cervical Cancer using a Probabilistic Neural Network	2019
13	M. Kusy	Selection of pattern neurons for a probabilistic neural network by means of clustering and nearest neighbor techniques	2019