Intelligent data processing Field of study: Computer Science
Programme code: W4-N2IN19.2021

Module name: intelligent data processing
Module code: W4-IN-N2-20-F-IPD
Programme code: W4-N2IN19.2021
Semester:
  • winter semester 2023/2024
  • summer semester 2022/2023
  • winter semester 2022/2023
  • summer semester 2021/2022
Language of instruction: Polish
Form of verification: course work
ECTS credits: 4
Description:
The goal is to introduce the student to methods of data mining, classification tasks, clustering and rule induction process. It also includes the basics of fuzzy inference or deep learning with elements of neural networks.
Prerequisites:
(no information given)
Key reading:
JOHN A. HARTIGAN, „Clustering Algorithms „ Department of Statisties Yaie University , JOHN VVILEY & SONS, 1975 Tadeusz Morzy , „Eksploracja danych : metody i algorytmy”, Warszawa, 2013, PWN. Andrew W. Trask, „Zrozumieć głębokie uczenie”, 2019, PWN Larose Daniel „Metody i modele eksploracji danych”. PWN, 2008 Hand David, Manila Heikki, Smyth Padhraic, „Principles of data mining”, MIT Press, 2001. Morzy Tadeusz, „Eksploracja danych”, PWN, 2013 Gatnar Eugeniusz, Walesiak Marek, „Statystyczna analiza danych z wykorzystaniem programu R”, PWN, 2009
Learning outcome of the module Codes of the learning outcomes of the programme to which the learning outcome of the module is related [level of competence: scale 1-5]
Is aware of the possibilities offered by intelligent data processing, especially in the context of large data sets. [M_001]
 K_K02 [1/5]
Has knowledge of the basics of artificial intelligence, including fuzzy logic and fuzzy inference [M_002]
 K_W02 [2/5] K_W04 [3/5] K_W09 [2/5]
Has knowledge of data mining in the context of detecting dependencies and patterns (e.g. rules) in data as well as atypical data. [M_003]
 K_W02 [1/5] K_W04 [2/5] K_W08 [2/5] K_W09 [4/5]
Has knowledge of the basics of artificial neural networks and so-called Deep learning. [M_004]
 K_W02 [2/5] K_W04 [2/5] K_W08 [2/5] K_W09 [3/5]
Is able to implement or manually perform calculations and operations of fuzzyfication, fuzzy inference and defuzzyfication [M_005]
 K_U03 [3/5] K_U07 [2/5] K_U08 [2/5] K_U09 [3/5]
Is able to apply the selected algorithm of rule induction (e.g. decision trees, association rules) for any data set or detection of unusual cases. [M_006]
 K_U01 [1/5] K_U03 [3/5] K_U08 [2/5] K_U09 [3/5]
Can use a dedicated tool to create a neural network model and interpret learning results of the created model for any data set. [M_007]
 K_U03 [3/5] K_U07 [2/5] K_U08 [2/5] K_U09 [3/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
exam (test) [W_001]
Knowledge verification based on the content presented in the lecture. The exam consists of both open and closed theory questions.
 M_001 M_002 M_003 M_004 M_005 M_006 M_007
Projects and reports [W_002]
Developing projects with reports for them within a specified period as a verification of the skills acquired in solving problems.
 M_001 M_005 M_006 M_007
Form of teaching Student's own work Assessment of the learning outcomes
Type Description (including teaching methods) Number of hours Description Number of hours
lecture [Z_001]
Transferring the content of education in verbal form using audiovisual means and other written teaching aids
 15
Preparation for the exam
 15 exam (test) [W_001]
laboratory classes [Z_002]
Preparing students to solve tasks with an indication of the methodology of the procedure, an indication of the order of performed activities.
 30
Students independently solve tasks assigned to the laboratory, prepare reports for their projects.
 60 Projects and reports [W_002]
Attachments
Module description (PDF)
Information concerning module syllabuses might be changed during studies.
Syllabuses (USOSweb)
Semester Module Language of instruction
(no information given)