• A
  • A
  • A
  • ABC
  • ABC
  • ABC
  • А
  • А
  • А
  • А
  • А
Regular version of the site

Practical Machine Learning Methods for Data Mining

2019/2020
Academic Year
ENG
Instruction in English
5
ECTS credits
Course type:
Compulsory course
When:
1 year, 4 module

Instructor

Course Syllabus

Abstract

During this practically oriented data science module students will learn how machine learning uses computers to run predictive models. The main principal is to explore existing data to build new knowledge, forecast future behaviour, anticipate outcomes and trends. Explore theory and practice, and work with tools like R and Python to solve advanced data science problems.
Learning Objectives

Learning Objectives

  • Choose methods adequately corresponding to the objectives of a research project
  • Collect, store, process and analyze data automatically with the use of scripting languages according to high standards
  • Conduct empirical research in management and marketing using modern analytic software tools
  • Develop and apply new research methods of basic machine learning algorithms and ways to collect information using data mining techniques
  • Solve economic and managerial problems using best practices of data analysis using modern computational tools
  • Able to choose tools, modern technical means and information technologies for processing information in accordance with the assigned scientific task in the field of management
  • Can identify the data needed for addressing the management and business objectives; to collect data and process it
Expected Learning Outcomes

Expected Learning Outcomes

  • Choose methods adequately corresponding to the objectives of a research project
  • Collect, store, process and analyze data automatically with the use of scripting languages according to high standards
  • Conduct empirical research in management and marketing using modern analytic software tools
  • Develop and apply new research methods of basic machine learning algorithms and ways to collect information using data mining techniques
  • Solve economic and managerial problems using best practices of data analysis using modern computational tools
  • Able to choose tools, modern technical means and information technologies for processing information in accordance with the assigned scientific task in the field of management
Course Contents

Course Contents

  • Introduction to Data Science
  • Data Sources and Data Structures
  • Data Collection and Data Processing
  • Data Collection and Data Processing
  • Machine Learning Algorithms
  • Practical Use of ML Algorithms
Assessment Elements

Assessment Elements

  • non-blocking Online-test
  • non-blocking Individual homework
  • non-blocking Individual seminar coding tasks
  • non-blocking Exam
Interim Assessment

Interim Assessment

  • Interim assessment (4 module)
    0.3 * Exam + 0.3 * Individual homework + 0.1 * Individual seminar coding tasks + 0.3 * Online-test
Bibliography

Bibliography

Recommended Core Bibliography

  • Muller, A. C., & Guido, S. (2017). Introduction to machine learning with Python: a guide for data scientists. O’Reilly Media. (HSE access: http://ebookcentral.proquest.com/lib/hselibrary-ebooks/detail.action?docID=4698164)

Recommended Additional Bibliography

  • Cirillo, A. (2017). R Data Mining. Birmingham, UK: Packt Publishing. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=nlebk&AN=1643003