Machine learning in biometrics and bioinformatics Field of study: Computer Science
Programme code: W4-S2INA19.2020

Module name: Machine learning in biometrics and bioinformatics
Module code: W4-INA-S2-20-F-UMwBB
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 course aims at acquainting the students with machine learning algorithms, with particular emphasis on their applications in biometrics and bioinformatics. It includes the discussion on different learning methods with and without supervision. The primary 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 can solve problems individually or in a team, using the gained 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 can analyse any biometric system to use 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]
The student has 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]
The student knows selected neural network architectures. [M_004]
 K_W01 [1/5] K_W04 [1/5] K_W09 [1/5]
The student can implement machine learning models for data classification and regression problems in biometrics and bioinformatics using the software libraries. [M_005]
 K_W01 [1/5] K_W02 [1/5] K_W04 [1/5] K_W05 [1/5] K_W09 [1/5]
The student can test the advancement of his work or teamwork and refer to it. [M_006]
 K_U03 [1/5] K_U04 [1/5] K_U05 [1/5]
The student is familiar with the current state and the latest developments and trends in computer science, including artificial intelligence and machine learning methods and their biometrics and bioinformatics applications. [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]
The students solve a theoretical test related to the topics discussed in the lectures.
 M_003 M_004 M_007
Project documentation [W_002]
The students presentat a 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]
The lectures are conducted verbally with the use of content visualisation, emphasising the material particularly difficult to understand. The students are encouraged by asking questions about the content. The classes have both traditional and e-learning form.
 15
The students prepare for the test.
 10 Test [W_001]
laboratory classes [Z_002]
During the laboratory classes, the students learn about mathematical models of machine learning and solve tasks in this field. The classes have both a traditional and e-learning form.
 30
The students complete tasks from individual topics with analysis of 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)