Machine learning in biometrics and bioinformatics Field of study: Computer Science
Programme code: W4-S2IN19.2022

Module name: Machine learning in biometrics and bioinformatics
Module code: W4-IN-S2-20-F-UMwBiB
Programme code: W4-S2IN19.2022
Semester:
  • summer semester 2025/2026
  • winter semester 2025/2026
  • summer semester 2024/2025
  • winter semester 2024/2025
  • summer semester 2023/2024
  • winter semester 2023/2024
Language of instruction: Polish
Form of verification: course work
ECTS credits: 4
Description:
The aim of this course is to familiarize students with machine learning algorithms, with particular emphasis on their applications in biometrics and bioinformatics. Different learning methods with and without supervision will be discussed. The main element of the course are methods based on neural networks.
Prerequisites:
(no information given)
Key reading:
1. Jacek Koronacki, Jan Ćwik: Statystyczne systemy uczące się. Wydanie drugie, EXIT, Warszawa, 2007 2. Gareth James, Daniela Witten, Trevor Hastie, Robert Tibshirani: An Introduction to Statistical Learning with Applications in R, Springer, 2013 3. Ian Goodfellow, Yoshua Bengio, Aaron Courville: Deep Learning. Systemy uczące się, PWN, Warszawa, 2018 4. Brian Steele, John Chandler, Swarna Reddy: Algorithms for Data Science, Springer, 2016 5. Deep Learning in Biometrics, Mayank Vatsa, Richa Singh, Angshul Majumdar, CRC Press, 2018 6. Deep Learning for Biometrics, Bir Bhanu, Ajay Kumar, Springer Publishing Company, 2017
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 should have the ability to solve problems individually or in a team, using the acquired knowledge and practical skills. [M_001]
 K_U01 [1/5] K_U02 [1/5] K_K01 [1/5] K_K03 [1/5] K_K04 [1/5]
The student is able to analyze any biometric system in terms of using the machine learning algorithm. [M_002]
 K_W01 [1/5] K_W02 [1/5] K_W04 [1/5] K_W05 [1/5] K_W09 [1/5] K_U01 [1/5] K_U08 [1/5] K_U09 [1/5]
He has an in-depth knowledge of contemporary methods of artificial intelligence. [M_003]
 K_W01 [1/5] K_W02 [1/5] K_W05 [1/5] K_W09 [1/5] K_U01 [1/5]
He knows selected neural network architectures. [M_004]
 K_W01 [1/5] K_W04 [1/5] K_W09 [1/5]
The student is able to implement, using the necessary software libraries, machine learning models for problems of data classification and regression in biometrics and bioinformatics. [M_005]
 K_W01 [1/5] K_W02 [1/5] K_W04 [1/5] K_W05 [1/5] K_W09 [1/5]
The student is able to evaluate and refer to the degree of advancement of his work or teamwork. [M_006]
 K_U03 [1/5] K_U04 [1/5] K_U05 [1/5]
The student is familiar with the current state and latest developments and trends in computer science, including artificial intelligence, machine learning methods, including their applications in biometrics and bioinformatics. [M_007]
 K_W01 [1/5] K_W02 [1/5] K_W09 [1/5]
Type Description Codes of the learning outcomes of the module to which assessment is related
Test [W_001]
Solving a theoretical test related to the topics discussed in the lecture.
 M_003 M_004 M_007
Project documentation [W_002]
Presentation of full documentation of the project, including all stages of its implementation.
 M_001 M_002 M_003 M_004 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]
Presentation of educational content in verbal form with the use of content visualization. Focusing on material that is difficult to understand. Activating students by asking questions about the content. Classes in a traditional form, and e-learning.
 15
Preparing for the colloquium.
 10 Test [W_001]
laboratory classes [Z_002]
During laboratory classes, students learn about mathematical models of machine learning and solve tasks in this field. Classes in a traditional form, and e-learning.
 30
Solving tasks from individual topics with analysis of already existing solutions
 65 Project documentation [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)