Introduction to data classificatin and clusterization in biometry Field of study: Computer Science
Programme code: W4-S2INA19.2020

Module name: Introduction to data classificatin and clusterization in biometry
Module code: W4-INA-S2-20-F-WDZKK
Programme code: W4-S2INA19.2020
Semester:
  • summer semester 2021/2022
  • winter semester 2021/2022
  • summer semester 2020/2021
Language of instruction: English
Form of verification: course work
ECTS credits: 4
Description:
The module acquaints 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]
The student can choose an 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]
The 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]
The student can implement primary 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
Short test [W_001]
The short test in a traditional form (or on-line test) verifies knowledge from the lecture and laboratory classes.
 M_001 M_002
Project [W_002]
The student prepares the biometric system or test environment for the biometric system, with technical documentation.
 M_001 M_002 M_003
Final test [W_003]
The student writes the final 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]
The lectures are conducted using multimedia presentations in a traditional and e-learning form.
 15
The students should study auxiliary materials and literature.
 15 Final test [W_003]
laboratory classes [Z_002]
The project/lab classes take place in the computer laboratory and as e-learning.
 30
The students study the literature and on-line materials and prepare the projects.
 60 Short test [W_001] 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)