Annotation

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The course aims to acquaint students with the basic methods and algorithms for machine learning, through which analysis and detection of dependencies in data sets, prediction of values, model building and more. Lecture topics cover basic methods and algorithms of machine learning such as regression analysis, algorithm of the nearest neighbors (k-Nearest Neighbors), algorithm with supporting vectors (Support Vector Machines), neural networks, principal component analysis. During the exercises, applications will be developed to solve some typical tasks in the field of machine learning such as building models and predicting values, tasks for grouping and clustering data, image recognition, text analysis, spam detection in e-mail.

Content

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1. Introduction to machine learning - essence and goals

2. Linear regression with many variables

3. Logarithmic regression

4. Neural networks for model representation

5. Neural network training

6. Design of machine learning systems

7. Classification by algorithm of the nearest neighbors (k-Nearest Neighbors)

8. Classification by Support Vector Machines

9. Training without a supervisor

10. Classification with probability level

11. Principled component analysis

12. Detection of anomalies

13. Recommended systems

14. Machine learning in the presence of large amounts of data

15. A practical example of the application of machine learning