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Course Syllabus

Master 2023/2024

Research Seminar "Introduction to Specialty"

Type: Compulsory course
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: Maksim Karpov, Ekaterina Trofimova
Master’s programme: Магистр по наукам о данных (о)
Language: English
ECTS credits: 12
Contact hours: 40

Course Syllabus

Full Syllabus

Abstract

The discipline Master's Seminar takes place during the whole course of study and is compulsory for students of the Study Program "Master of Data Science";. In the course of communication with the academic supervisor, the student forms his/her individual study plan, receives additional feedback for the master's thesis.
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
  • Machine learning : beginner's guide to machine learning, data mining, big data, artificial intelligence and neural networks, Trinity, L., 2019
  • Machine learning : the art and science of algorithms that make sense of data, Flach, P., 2014
  • 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
  • Foundations of machine learning, Mohri, M., 2012