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Course Syllabus

Master 2023/2024

Research Seminar "Introduction to Specialty"

Type: Compulsory course (Master of Data Science)
Area of studies: Applied Mathematics and Informatics
Delivered by: Big Data and Information Retrieval School
Where: Faculty of Computer Science
When: 1 year, 1-4 module
Mode of studies: offline
Open to: students of one campus
Instructors: Oleg Melnikov
Master’s programme: Master of Data Science
Language: English
ECTS credits: 12
Contact hours: 40

Course Syllabus

Full Syllabus

Abstract

The research seminar provides an opportunity to learn about applications of machine learning in various areas of science. The purpose of the research seminar is to expand the research horizons of students. It is expected that at the end of the course, the student will be able to prepare a theoretical research paper.
Learning Objectives

Learning Objectives

  • The aims of the discipline are: (1) to form an individual curriculum, (2) to choose a theme of the master's thesis
Expected Learning Outcomes

Expected Learning Outcomes

  • know the rules of writing master thesis
  • prepare a brief of the master thesis
  • knows the key stages and deadlines for master thesis preparation and delivery
  • An individual education plan for the second year
  • Correcting the syllabus and term or master's thesis work
  • Selecting a track
Course Contents

Course Contents

  • Individual academic trajectories
  • The master's thesis
Assessment Elements

Assessment Elements

  • non-blocking Quizzes (part 1)
  • non-blocking Homework (part 1)
  • non-blocking Ethics (part 1)
  • non-blocking Participation (part 1)
  • non-blocking Presentation (part 2)
  • non-blocking Quizzes (part 2)
Interim Assessment

Interim Assessment

  • 2023/2024 2nd module
    0.1 * Ethics (part 1) + 0.4 * Homework (part 1) + 0.1 * Participation (part 1) + 0.4 * Quizzes (part 1)
  • 2023/2024 4th module
    0.5 * Presentation (part 2) + 0.5 * Quizzes (part 2)
Bibliography

Bibliography

Recommended Core Bibliography

  • A Tutorial on Machine Learning and Data Science Tools with Python. (2017). Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.E5F82B62
  • Rogers, S., & Girolami, M. (2016). A First Course in Machine Learning (Vol. 2nd ed). Milton: Chapman and Hall/CRC. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsebk&AN=1399490

Recommended Additional Bibliography

  • A first course in machine learning, Rogers, S., 2012