• A
  • A
  • A
  • АБB
  • АБB
  • АБB
  • А
  • А
  • А
  • А
  • А
Обычная версия сайта
Бакалавриат 2018/2019

Введение в машинное обучение на языке Python

Лучший по критерию «Новизна полученных знаний»
Статус: Курс по выбору (Менеджмент)
Направление: 38.03.02. Менеджмент
Когда читается: 3-й курс, 1, 2 модуль
Формат изучения: Full time
Язык: английский
Кредиты: 4

Course Syllabus

Abstract

When solving various business tasks, it has to deal with the need to process large amounts of information. To work with Big data it needs to own a variety of technologies that allows to use machine learning algorithms. Python language contains a number of built-in libraries for working with data, developing machine learning algorithms, and is supported by many modern platforms Apache Spark, Microsoft Azure, etc.
Learning Objectives

Learning Objectives

  • The objectives of the course is to develop students ' complex theoretical knowledge and methodological foundations in the field of machine learning, as well as practical skills for working with big data using Python.
Expected Learning Outcomes

Expected Learning Outcomes

  • To know: basic terms and concepts of Python language; basic terms and concepts of machine learning; To have practical skills of: using built-in Python libraries; developing machine learning algorithms in Python; solving various business tasks for processing large amounts of information. To acquire basic knowledge of: tools and modern software platforms that support the implementation of machine learning algorithms;
Course Contents

Course Contents

  • Introduction. Python basics.
    Enthought Canopy Express development environment. Basic concepts of the Python language. Run Python scripts.
  • Statistics and Probability Refresher, and Python Practise
    Data type. Expectation, median, mode, standard deviation, variance. Distribution functions, probability density. Percentiles and moments. Covariance and correlation. Conditional probability. Bayes theorem.
  • Predictive Models
    Regression Algorithms. Multilevel models.
  • Machine Learning with Python
    Training with a teacher and without a teacher. Overfitting. Bayesian methods. Clustering. Entropy change. Decision tree. Ensemble learning. SVM. K-nearest neighbor method. Dimension reduction. Principal components analysis method.
  • Recommender Systems
    User-Based Collaborative Filtering. Item-Based Collaborative Filtering
  • Dealing with Real-World Data
    Cross-validation for K blocks. Data cleaning and normalization. The detection of outliers
  • Apache Spark Machine Learning on Big Data
    The Concept Of Apache Spark. RDD. Introduction to MLLib
Assessment Elements

Assessment Elements

  • non-blocking Homework
  • non-blocking Control work
  • non-blocking Oral exam
  • non-blocking Activity on seminars
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.2 * Activity on seminars + 0.18 * Control work + 0.12 * Homework + 0.5 * Oral exam
Bibliography

Bibliography

Recommended Core Bibliography

  • Haroon, D. (2017). Python Machine Learning Case Studies : Five Case Studies for the Data Scientist. [Berkeley, CA]: Apress. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1623520
  • Idris, I. (2016). Python Data Analysis Cookbook. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1290098

Recommended Additional Bibliography

  • Baka, B. (2017). Python Data Structures and Algorithms. Birmingham, U.K.: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1528144
  • Bill Lubanovic. (2019). Introducing Python : Modern Computing in Simple Packages. [N.p.]: O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=2291494
  • Vanderplas, J. T. (2016). Python Data Science Handbook : Essential Tools for Working with Data (Vol. First edition). Sebastopol, CA: Reilly - O’Reilly Media. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1425081
  • Vanderplas, J.T. (2016). Python data science handbook: Essential tools for working with data. Sebastopol, CA: O’Reilly Media, Inc. https://proxylibrary.hse.ru:2119/login.aspx?direct=true&db=nlebk&AN=1425081.