Магистратура 2023/2024

Научно-исследовательский семинар "Введение в специальность"

Статус: Курс обязательный (Магистр по наукам о данных)

Направление: 01.04.02. Прикладная математика и информатика

Кто читает: Департамент больших данных и информационного поиска

Где читается: Факультет компьютерных наук

Когда читается: 1-й курс, 1-4 модуль

Формат изучения: без онлайн-курса

Охват аудитории: для своего кампуса

Преподаватели: Карпов Максим Евгеньевич, Трофимова Екатерина Алексеевна

Прогр. обучения: Магистр по наукам о данных (о)

Язык: английский

Кредиты: 12

Контактные часы: 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

Course Contents

• Individual academic trajectories
• The master's thesis

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 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

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

Authors

• Burova Margarita Borisovna
• Литвишкина Ален Витальевна
• Боднарук Иван Иванович