Intelligent data processing Field of study: Computer Science
Programme code: W4-S2INA19.2021

Module name: Intelligent data processing
Module code: W4-INA-S2-20-F-IPD
Programme code: W4-S2INA19.2021
Semester:
  • summer semester 2022/2023
  • winter semester 2022/2023
  • summer semester 2021/2022
Language of instruction: English
Form of verification: course work
ECTS credits: 4
Description:
The aim is to introduce the student to data mining methods, 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]
The student is aware of intelligent data processing possibilities, especially in large data sets. [M_001]
 K_K02 [1/5]
The student knows 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]
The student has a knowledge of data mining in detecting dependencies and patterns (e.g. rules) in regular and atypical data. [M_003]
 K_W02 [1/5] K_W04 [2/5] K_W08 [2/5] K_W09 [4/5]
The student knows the basics of artificial neural networks and deep learning. [M_004]
 K_W02 [2/5] K_W04 [2/5] K_W08 [2/5] K_W09 [3/5]
The student can implement or manually perform calculations and operations of fuzzification, fuzzy inference and defuzzification. [M_005]
 K_U03 [3/5] K_U07 [2/5] K_U08 [2/5] K_U09 [3/5]
The student can apply the selected rule induction algorithm (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]
The student knows how to use a dedicated tool to create a neural network model and interpret the developed model's learning results 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]
The exam checks the knowledge gained in the lectures. The test comprises both open and closed-ended theoretical questions.
 M_001 M_002 M_003 M_004 M_005 M_006 M_007
Project reports [W_002]
The students develop projects with reports within a deadline, which is to verify the skills gained while solving the tasks.
 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]
The content will be provided in verbal form with support of various audiovisual means and also other teaching aids.
 15
The students prepare for the exam.
 15 Exam (Test) [W_001]
laboratory classes [Z_002]
The classes prepare students to complete tasks with the emphasis on the method and the sequence of operations.
 30
The students independently solve tasks assigned to the classes and prepare reports for their projects.
 60 Project 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)