Бакалавриат
2026/2027
Построение скоринговых моделей с использованием методов машинного обучения
ID 1137924
Статус:
Курс по выбору (Программная инженерия)
Кто читает:
Департамент программной инженерии
Где читается:
Факультет компьютерных наук
Когда читается:
3-й курс, 3, 4 модуль
Охват аудитории:
для всех кампусов НИУ ВШЭ
Язык:
английский
Кредиты:
5
Контактные часы:
60
Course Syllabus
Abstract
This course describes how dramatically changes of the information market provides new power tools for financial data processing and analysis. In addition, we will compare classical and contemporary tools performance. The main aim of this course is a species of different country data sets and technics for integration this data for common international data environment.
Learning Objectives
- The main aims of the course are providing knowledge about all types of public and private scoring systems, data sets for their creation and appropriate ML methods suitable for scoring development.
Expected Learning Outcomes
- Providing knowledge about all types of public and private scoring systems, data sets for their creation and appropriate ML methods suitable for scoring development.
Course Contents
- Contemporary financial analyses main challenges.
- Big, data mining, data science.
- Main data types.
- Correlation.
- Data processing. Modeling..
- Estimation and model testing.
- Practice.
- Interpretable models and suggestion systems.
- Applications. Scorings, rankings, ratings.
- Modern challenges.
Interim Assessment
- 2026/2027 4th module0.5 * Final exam + 0.3 * Home assignments + 0.2 * Case discussions
Bibliography
Recommended Core Bibliography
- Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning, 2016. URL: http://www.deeplearningbook.org
Recommended Additional Bibliography
- Искусственный интеллект и машинное обучение - Митяков Е. С., Шмелева А. Г., Ладынин А. И. - Издательство "Лань" - 978-5-507-51198-3 - 2026 - русский - https://e.lanbook.com/book/507451 - ЛАНЬ - 507451