| Course title | Introduction to Machine Learning |
|---|---|
| Course code | KI/EUSU |
| Organizational form of instruction | Lecture + Lesson |
| Level of course | Bachelor |
| Year of study | not specified |
| Semester | Summer |
| Number of ECTS credits | 5 |
| Language of instruction | English |
| Status of course | unspecified |
| Form of instruction | Face-to-face |
| Work placements | This is not an internship |
| Recommended optional programme components | None |
| Course availability | The course is available to visiting students |
| Lecturer(s) |
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| Course content |
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1. Division of machine learning tasks 2. Data classification, types of classifiers 3. Preparation of data and datasets: replacement of missing data, work with categorical data 4. Issues of data dimensionality and methods of its reduction 5 Decision trees (types of metrics, construction) 6. Linear classification, linear separability, linear perceptron and its learning, logistic regression 7. Support vector machines (SVM): problem formulation, SVM as an optimization task 8. Method of support vectors: soft-margin issue, dual SVM formulation, solution using quadratic programming, SMO algorithm 9. Method of support vectors: kernel transformations, types of kernels 10. Neural networks: types of networks, network learning, activation functions 11. Neural networks: nonlinear multilayer perceptron (MLP) and its properties, backpropagation algorithm 12-13. Deep learning: basic principles (convolution, pooling) and practical use of frameworks 14. Evaluation of seminar work and discussion
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| Learning activities and teaching methods |
| unspecified |
| Learning outcomes |
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This course presents a practical introduction to data processing and analysis via machine learning. We are focused on a basic understanding of the principles of the methods and we emphasise the practical application of the methods. The relevant frameworks in the Python language (Scikit-learn, TensorFlow, Keras, CVXOPT) are used.
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| Prerequisites |
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Basics from linear algebra (vectors, matrices, vector spaces) and analysis and basics of Python
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| Assessment methods and criteria |
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unspecified
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| Recommended literature |
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| Study plans that include the course |