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Магистратура 2017/2018

Методы и технологии работы с потоковыми и неструктурированными данными

Статус: Курс по выбору (Компьютерные системы и сети)
Направление: 09.04.01. Информатика и вычислительная техника
Когда читается: 2-й курс, 1, 2 модуль
Формат изучения: без онлайн-курса
Прогр. обучения: Компьютерные системы и сети
Язык: английский
Кредиты: 5
Контактные часы: 48

Course Syllabus

Abstract

This course focuses on application of economic models, statistical methods, and machine learning technologies to design of corporate and government decision support systems. Machine learning receives special emphasis in this course due to its central role played in modern analytical applications.
Learning Objectives

Learning Objectives

  • The aim of the present course is to provide a solid foundation in the aforementioned domains of knowledge so that a good student by the end of this course is well equipped to design an adequate model for a corporation or the government using data analysis if necessary.
Expected Learning Outcomes

Expected Learning Outcomes

  • The ability to reflect developed methods of activity.
  • The ability to independently become acquainted with new research methods, to change scientific profile of activity.
  • The ability to analyze, verify and evaluate the completeness of information, if necessary complete and generate missing data, work under conditions of uncertainty.
  • The ability to conduct professional (including research) activity in international environment.
  • The ability to communicate orally and in written form in English in the frame of professional and scientific intercourse.
  • The ability to describe problems and situations of professional activity in terms of humanitarian, economic and social sciences to solve problems occurring across sciences, in allied professional fields.
  • The ability to detect, transmit common goals in the professional and social activities.
Course Contents

Course Contents

  • Conceptual framework of a Decision Support System: information needs, data and information.
  • Overview of machine learning techniques.
  • Decision trees, random forests, bagging and boosting.
  • Anomaly detection, recommender systems.
  • K means, principal component analysis.
  • Neural networks.
  • Practical issues in machine learning. Support vector machines.
  • Econometrics: linear and logistic regression.
  • Economic modelling and statistics.
Assessment Elements

Assessment Elements

  • non-blocking Midterm Homework
  • Partially blocks (final) grade/grade calculation Intermediate Еxam
  • non-blocking Final project - Written research summary
  • Partially blocks (final) grade/grade calculation Final Exam
Interim Assessment

Interim Assessment

  • Interim assessment (1 module)
    0.6 * Intermediate Еxam + 0.4 * Midterm Homework
  • Interim assessment (2 module)
    0.6 * Final Exam + 0.4 * Final project - Written research summary
Bibliography

Bibliography

Recommended Core Bibliography

  • Системный анализ : учебник для вузов, Антонов, А. В., 2006
  • Системный анализ, оптимизация и принятие решений : учеб. пособие для вузов, Козлов, В. Н., 2010
  • Теория систем и системный анализ : учебник для вузов, Волкова, В. Н., 2010

Recommended Additional Bibliography

  • Hastie, T., Tibshirani, R., Friedman, J. The elements of statistical learning: Data Mining, Inference, and Prediction. – Springer, 2009. – 745 pp.