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
Master’s programme:
Магистр по наукам о данных (о)
Language:
English
ECTS credits:
12
Contact hours:
40
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
Assessment Elements
- Quizzes (part 1)
- Homework (part 1)
- Ethics (part 1)
- Participation (part 1)
- Presentation (part 2)
- Quizzes (part 2)
Interim Assessment
- 2023/2024 2nd module0.1 * Ethics (part 1) + 0.4 * Homework (part 1) + 0.1 * Participation (part 1) + 0.4 * Quizzes (part 1)
- 2023/2024 4th module0.5 * Presentation (part 2) + 0.5 * Quizzes (part 2)
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