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Machine Learning

2021/2022
Учебный год
ENG
Обучение ведется на английском языке
3
Кредиты
Статус:
Курс обязательный
Когда читается:
2-й курс, 1, 2 модуль

Преподаватель

Course Syllabus

Abstract

It is a compulsory discipline. The purpose of mastering the discipline "Machine Learning" is to develop students 'theoretical knowledge and practical skills on the basics of machine learning, mastering students' tools, models and methods of machine learning, as well as acquiring the skills of a data scientist and developer of mathematical models, methods and analysis algorithms data. As a result of mastering the discipline, the student must: - Know the key concepts, goals and objectives of using machine learning; methodological foundations of the application of machine learning algorithms. - Be able to visualize the results of machine learning algorithms, choose a machine learning method appropriate to the research task, and interpret the results. - Have the skills (gain experience) of reading and analyzing academic literature on the application of machine learning methods, building and evaluating the quality of models.
Learning Objectives

Learning Objectives

  • 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.
Expected Learning Outcomes

Expected Learning Outcomes

  • 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.
Course Contents

Course Contents

  • Data Dimension Analysis and Working with Features
    Data 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 Analysis
    Analysis 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 Results
    Interpreting 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.
Assessment Elements

Assessment Elements

  • non-blocking Project
    Students 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.
  • blocking Exam
    The 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.
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.5 * Exam + 0.5 * Project
Bibliography

Bibliography

Recommended Core Bibliography

  • 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

Recommended Additional Bibliography

  • 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