Introduction to data classificatin and clusterization in biometry Field of study: Computer Science
Programme code: W4-S2IN19.2021

Module name: Introduction to data classificatin and clusterization in biometry
Module code: W4-IN-S2-20-F-WDZKKD
Programme code: W4-S2IN19.2021
Semester:
  • 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 module is dedicated to familiarizing the student with the basic algorithms for classification and clustering of data used in biometric systems.
Prerequisites:
(no information given)
Key reading:
Ian H. Witten, Eibe Frank, Data Mining. Practical Machine Learning Tools and Techniques, Morgan Kaufmann Pub., 2005.
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]
Student can choose adequate classification or clustering algorithm to solve a given biometric problem. [M_001]
 K_W01 [1/5] K_W02 [1/5] K_W04 [1/5] K_W05 [1/5] K_W09 [1/5] K_U03 [1/5] K_U08 [1/5] K_U09 [1/5]
Student can design tests for biometric based identification/verification system. [M_002]
 K_W04 [1/5] K_W09 [1/5] K_U01 [1/5] K_U05 [1/5] K_U09 [1/5] K_K02 [1/5] K_K04 [1/5]
Student can implement basic classification and clustering algorithms, used in biometry. [M_003]
 K_W01 [1/5] K_W04 [1/5] K_W05 [1/5] K_U01 [1/5] K_U02 [1/5] K_U05 [1/5] K_U08 [1/5] K_U09 [1/5] K_U10 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Small exam [W_001]
Short exam (or on-line test), verifying knowledge derived from the lecture and laboratories.
 M_001 M_002
Passing project [W_002]
Project of the biometric system or test environment for the biometric system, with a technical documentation.
 M_001 M_002 M_003
Passing test [W_003]
Passing test covering the whole topic.
 M_001 M_002 M_003
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]
Classes are run as a lecture (15 hours) with use of a multimedia presentations. Classes in a traditional form, and e-learning.
 15
Student should study auxiliary materials, and the literature.
 15 Passing test [W_003]
laboratory classes [Z_002]
Project/lab classes in computer laboratory, and e-learning.
 30
Literature and on-line study, and preparing a passing project.
 60 Small exam [W_001] Passing project [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)