Machine Learning
Teachers in charge
Course code
230132
Study Module
- TTI - graduate track - Engineering Design and Management of Textiles
- TTI - graduate track - Textile Chemistry, Materials, and Ecology
- TTI - graduate track - Clothing Engineering
- TTI - graduate track - Textile Design for Industry
- TTI - graduate track - Clothing Design for Industry
Schedule ∑ (P+V+S)
4 (2+2+0)
ECTS
4
Knowledge verification
Colloquium, written exam, oral exam
Precondition for testing
Lecture type
Lectures, exercises, seminars
Exercise type
Auditory, laboratory
Learning outcomes
the students will be able to:
- Explain the basic theoretical concepts of machine learning, the working principles of selected machine learning algorithms, and choose an appropriate algorithm to solve a given problem.
- Apply machine learning algorithms to practical examples from the textile and clothing industry and other engineering fields using the Weka software system.
- Analyze data sets and choose appropriate options in the Weka software program to solve a given problem.
- Model their own algorithms suitable for solving a given problem on the provided data set using the Weka software system.
- Interpret the results of the algorithms (efficiency and other parameters) for a specific data set in the Weka software system.
Subject content
Introduction to the basic concepts of machine learning and a brief overview of the most popular machine learning algorithms. Introduction to computer applications for solving machine learning problems. Introduction to the concept of training and testing sets, the importance of repeated experiments, and the method for determining classifier efficiency. Introduction to the concept of cross-validation and interpretation of the results obtained in this way. Introduction to the OneR algorithm, the concept of overfitting, using probability in the construction of classifiers (Naive Bayes algorithm). Creation of decision trees, use of the linear regression algorithm. Introduction to data mining. Introduction to the Experimenter interface in the Weka program. Discretization of numerical attributes. Introduction to simple neural networks, the multilayer perceptron, and learning curves.
Aim of course
To train students to apply machine learning algorithms on large data sets using the Weka software system. To train students to independently model machine learning algorithms for solving a given problem. To train students to independently draw conclusions based on the analysis of algorithm results and to propose the implementation of these conclusions in the decision-making process.
Literature necessary for the course
- Witten, I. H., Frank, E., Hall, M. A., Pal, C. J., "Data Mining – Practical Machine Learning Tools and Techniques," Morgan Kaufmann, 2017, ISBN 9780128042915, https://doi.org/10.1016/B978-0-12-804291-5.00001-5.
- Han, J., Kamber, M., Pei, J., "Data Mining: Concepts and Techniques," 3rd edition, Morgan Kaufmann, 2011, ISBN 978-0-12-381479-1, https://doi.org/10.1016/C2009-0-61819-5.
Supplementary literature
- "Data Mining with Weka," MOOC available at: https://www.cs.waikato.ac.nz/ml/weka/mooc/dataminingwithweka/