Data Analysis and Machine Learning
Course description sheet
Basic information
- Field of study
- Modern Technologies in Forensic Science
- Major
- -
- Organisational unit
- Faculty of Computer Science, Electronics and Telecommunications
- Study level
- First-cycle (engineer) programme
- Form of study
- Full-time studies
- Profile
- General academic
- Didactic cycle
- 2025/2026
- Course code
- INKTS.Ii20.08565.25
- Lecture languages
- Polish
- Mandatoriness
- Obligatory
- Block
- Core Modules
- Course related to scientific research
- Yes
Lecturer
Maciej Wielgosz
|
Period
Semester 6
|
Method of verification of the learning outcomes
Completing the classes
Activities and hours
Lectures:
28
Project classes: 28 |
Number of ECTS credits
5
|
Goals
| C1 | Przekazanie wiedzy z zakresu technik i metod uczenia maszynowego. |
| C2 | Zapoznanie studentów z bieżącymi trendami w dziedzinie. |
| C3 | Uświadomienie słuchaczom problemów dotyczących analizy danych pochodzących z różnych źródeł. |
Course's learning outcomes
| Code | Outcomes in terms of | Learning outcomes prescribed to a field of study | Methods of verification |
| Knowledge – Student knows and understands: | |||
| W1 | Zna specyfikę oraz dobre praktyki pracy z danymi pochodzącymi z różnych źródeł. | NKT1A_W05, NKT1A_W09 | Activity during classes, Participation in a discussion |
| W2 | Zna metody i techniki uczenia maszynowego i ich zastosowanie w analizie danych. | NKT1A_W05, NKT1A_W09 | Activity during classes, Participation in a discussion |
| Skills – Student can: | |||
| U1 | Umie rozwiązywać zadane problemy poprzez dobór odpowiednich algorytmów uczenia maszynowego i ich wdrożenie z użyciem dostępnych bibliotek lub narzędzi wysokiego poziomu. | NKT1A_U04, NKT1A_U06, NKT1A_U07, NKT1A_U08 | Project |
| Social competences – Student is ready to: | |||
| K1 | Rozumie znaczenie, korzyści i trudności związane z łączeniem danych pochodzących z różnych źródeł. | NKT1A_K04 | Activity during classes, Participation in a discussion |
Student workload
| Activity form | Average amount of hours* needed to complete each activity form | |
| Lectures | 28 | |
| Project classes | 28 | |
| Preparation for classes | 15 | |
| Realization of independently performed tasks | 30 | |
| Contact hours | 5 | |
| Preparation of project, presentation, essay, report | 30 | |
| Student workload |
Hours
136
|
|
| Workload involving teacher |
Hours
56
|
|
* hour means 45 minutes
Program content
| No. | Program content | Course's learning outcomes | Activities |
| 1. |
Podstawowe pojęcia i klasyfikacja algorytmów uczenia maszynowego. |
W2 | Lectures |
| 2. |
Proces analizy danych w praktyce: dobór algorytmów i dobre praktyki. |
W1, W2, U1, K1 | Lectures, Project classes |
| 3. |
Bieżące trendy w uczeniu maszynowym. |
W1, W2, K1 | Lectures |
| 4. |
Analiza danych pochodzących z różnych źródeł. |
W1, K1 | Lectures, Project classes |
Extended information/Additional elements
Teaching methods and techniques :
E-learning, Lectures, Discussion, Group work, Project Based Learning
| Activities | Methods of verification | Credit conditions |
|---|---|---|
| Lectures | Activity during classes, Participation in a discussion | |
| Project classes | Activity during classes, Participation in a discussion, Project |
Additional info
Classes are conducted using innovative teaching methods developed during 2017-2019 in the POWR.03.04.00-00-D002/16 project, carried out by the Faculty of Computer Science, Electronics and Telecommunications under the Smart Growth Operational Programme 2014-2020.
Prerequisites and additional requirements
Basic knowledge of the Python language.
Rules of participation in given classes, indicating whether student presence at the lecture is obligatory
Lectures: Studenci uczestniczą w zajęciach poznając kolejne treści nauczania zgodnie z syllabusem przedmiotu. Studenci winni na bieżąco zadawać pytania i wyjaśniać wątpliwości. Rejestracja audiowizualna wykładu wymaga zgody prowadzącego.
Literature
Obligatory- Maciej Wielgosz, Machine Learning Primer, 2019. https://maciej.wielgosz.info/machine-learning-primer
- John D. Kelleher, Brian MacNamee, Aoife D’Arcy. Fundamentals of Machine Learning for Predictive Data Analytics: Algorithms, Worked Examples, and Case Studies. The MIT Press, 2015.
- Ian Goodfellow, Yoshua Bengio, Aaron Courville. Deep Learning. The MIT Press, 2016. https://www.deeplearningbook.org/
Scientific research and publications
Publications- Maciej Wielgosz, Andrzej Skoczeń, Matej Mertik. Using LSTM recurrent neural networks for detecting anomalous behavior of LHC superconducting magnets. Nuclear Inst. and Methods in Physics Research, A, 867:40–50, 2017. doi:10.1016/j.nima.2017.06.020
- Maciej Wielgosz, Matej Mertik, Andrzej Skoczeń, Ernesto De Matteis. The model of an anomaly detector for HiLumi LHC magnets based on Recurrent Neural Networks and adaptive quantization. Engineering Applications of Artificial Intelligence, 74:166–185,2018. ISSN 0952-1976. doi: 10.1016/j.engappai.2018.06.012
- Maciej Wielgosz, Andrzej Skoczeń. Using neural networks with data quantization for time series analysis in LHC superconducting magnets. International Journal of Applied Mathematics and Computer Science, 29(3):503–515, Sep 2019. ISSN 1641-876X. doi: 10.2478/amcs-2019-0037
- Maciej Wielgosz, Marcin Pietroń. Using Spatial Pooler of Hierarchical Temporal Memory to classify noisy videos with predefined complexity. Neurocomputing, 240:84 –97, May 2017. ISSN 0925-2312. doi: 10.1016/j.neucom.2017.02.046
- M. Markiewicz, M. Wielgosz, M. Bocheński, W. Tabaczyński, T. Konieczny, L. Kowalczyk. Predictive maintenance of induction motors using ultra-low power wireless sensors and compressed recurrent neural networks. IEEE Access, 7:178891–178902, Dec 2019.ISSN 2169-3536. doi: 10.1109/ACCESS.2019.2953019
- Maciej Wielgosz, Rafał Frączek, Paweł Russek, Marcin Pietroń, Agnieszka Dąbrowska-Boruch, Ernest Jamro, Kazimierz Wiatr. An experiment on the methods for the clustering and categorization of Polish text. Computing and Informatics, 36(1):186–204, 2017. doi: 10.4149/cai_2017_1_186
- Krzysztof Wróbel, Maciej Wielgosz, Aleksander Smywiński-Pohl, Marcin Pietroń. Comparison of SVM and Ontology-Based Text Classification Methods. W Artificial Intelligence and Soft Computing: 15th International Conference, ICAISC 2016, Zakopane, Poland, June 12-16, 2016, Proceedings, Part I, str. 667–680, Cham, 2016.Springer. ISBN 978-3-319-39378-0. doi: 10.1007/978-3-319-39378-0_57