- 1. The formation of students' theoretical knowledge and practical skills on the basics of machine learning.
- 2. Students mastering tools, models and methods of machine learning
- 3. Acquiring the skills of a data scientist and developer of mathematical models, methods and algorithms for data analysis.
- To know the key concepts, goals and objectives of using machine learning; methodological foundations for the application of machine learning algorithms.
- To be able to visualize the results of machine learning algorithms, to choose a machine learning method that matches the research task and interpret the results.
- To have the skills (to gain experience) of reading and analyzing academic literature on the application of machine learning methods, building and assessing the quality of models.
- Data Dimension Analysis and Working with FeaturesData dimension analysis. Effective dimension concept. Reducing the dimension of the data. Highlighting Important features. Working with signs. Working with sequences (text, sound, signal), images. Isolation of signs. Collaborative filtering.
- Data Distribution AnalysisAnalysis of the distribution of features in the data and sampling. Expectation Maximization (EM), Gaussian Sampling (GS), Markov Chain Monte Carlo Methods (MCMC). The complexity of machine learning models. Growth function. Vapnik-Chervonenkis theorem. Vapnik-Chervonenkis dimension. PAC-Bayes analysis.
- Interpreting ResultsInterpreting the results of machine learning. Target functions. Practical assessment of the quality of education. Theoretical assessment of the quality of education. Working with expert assessment of big data. Filtering expert assessment of big data. Expert Consent. Reputation of experts.
- ProjectStudents must apply the passed methods to parse one of the articles on machine learning. Any publications of machine learning conferences can be selected as articles.
- ExamThe exam is conducted in the format of parsing a scientific article on data analysis and machine learning. The examinee must demonstrate knowledge of the subject at a sufficient level to interpret contemporary scientific literature.
- Murphy, K. P. (2012). Machine Learning : A Probabilistic Perspective. Cambridge, Mass: The MIT Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=480968
- Trevor Hastie, Robert Tibshirani , et al., The Elements of Statistical Learning: Data Mining, Inference, and Prediction, 2nd edition, 2017. Free from the publisher: https://web.stanford.edu/~hastie/ElemStatLearn/printings/ESLII_print12.pdf