Master 2023/2024

Mentor's Seminar

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: 2 year, 1, 2 module

Mode of studies: offline

Open to: students of one campus

Master’s programme: Магистр по наукам о данных (заочная)

Language: English

ECTS credits: 4

Contact hours: 8

Course 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

• 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

• 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

• Individual academic trajectories
• The master's thesis

Assessment Elements

• Individual Education Plan
  Student should decide which courses are the best choice.
• Master Thesis
  Student should submit the application with details about the Master Thesis.

Interim Assessment

• 2022/2023 3rd module
  1 * Individual Education Plan
• 2023/2024 2nd module
  1 * Master Thesis

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