Outlier detection algorithms Field of study: Computer Science
Programme code: W4-S2INA19.2022

Module name: Outlier detection algorithms
Module code: W4-INA-S2-20-F-AWOwD
Programme code: W4-S2INA19.2022
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: English
Form of verification: course work
ECTS credits: 4
Description:
The goal is to introduce the listener to deviation detection algorithms so important in practical applications, e.g. for detecting embezzlement or unusual disease symptoms. Among the issues raised, there will be algorithms based on the distance between objects in the analysed space and algorithms derived from cluster analysis allowing identification of unmatched and ungroupable objects.
Prerequisites:
(no information given)
Key reading:
Aggarwal, Charu C., „Outlier Analysis”, 2013, Springer Mehrotra, Kishan G., Mohan, Chilukuri, Huang, Huaming, „Anomaly Detection Principles and Algorithms”, 2017, Springer Dhruba Kumar Bhattacharyya, Jugal Kumar Kalita, „Network Anomaly Detection: A Machine Learning Perspective, CRC Press 2014 Hawkins, D. , „Identification of Outliers”, Monographs on Statistics and Applied Probability, Springer, 1980 Tadeusz Morzy , „Eksploracja danych : metody i algorytmy”, Warszawa, 2013,PWN. Larose Daniel „Metody i modele eksploracji danych”. PWN, 2008 Hand David, Manila Heikki, Smyth Padhraic, „Principles of data mining”, MIT Press, 2001.
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 the essence of deviations in the data, which are not errors in the data but real different objects. [M_001]
 K_K02 [1/5]
The student knows the basics of descriptive statistics, including issues to identify deviations in data. [M_002]
 K_W04 [3/5] K_W09 [2/5]
The student knows methods of graphical data representation and detection of deviations in such representations. [M_003]
 K_W09 [3/5]
The student knows selected deviation detection algorithms, including algorithms based on distance and data distribution, and algorithms based on data density or local deviations. [M_004]
 K_W02 [2/5] K_W04 [2/5] K_W09 [3/5]
The student can choose the right algorithm to detect deviations depending on the type of analysed data [M_005]
 K_U01 [2/5] K_U03 [2/5] K_U08 [2/5] K_U09 [2/5]
The student can implement or use ready-made libraries/packages that allow a deviation detection algorithm for a selected data set. [M_006]
 K_U01 [2/5] K_U03 [2/5] K_U08 [2/5] K_U09 [4/5]
The student can determine the similarity/distance between two objects in multidimensional space. [M_007]
 K_U01 [2/5] K_U03 [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 test verifies knowledge based on the content presented in the lectures. The exam comprises both open and closed-ended theoretical questions.
 M_001 M_002 M_003 M_004 M_005 M_006 M_007
Projects and reports [W_002]
The students develop projects and write reports within a specified period to verify their skills in solving problems.
 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 lectures have verbal form and involve using audiovisual means and other written teaching aids.
 15
The students prepare for the exam.
 15 Exam (Test) [W_001]
laboratory classes [Z_002]
The classes thoroughly prepare the students to solve tasks, emphasising the method and the sequence of operations.
 30
The students prepare for the laboratory classes, complete the assigned tasks and write reports.
 60 Projects and 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)