Bachelor
2022/2023
Building Scoring Models Using Machine Learning Methods
Type:
Elective course (Software Engineering)
Area of studies:
Software Engineering
Delivered by:
School of Software Engineering
Where:
Faculty of Computer Science
When:
3 year, 3, 4 module
Mode of studies:
offline
Open to:
students of all HSE University campuses
Instructors:
Ilya Munerman
Language:
English
ECTS credits:
5
Contact hours:
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
- 2022/2023 4th module0.2 * Case discussions + 0.5 * Final exam + 0.3 * Home assignments
Bibliography
Recommended Core Bibliography
- Gareth James, Daniela Witten, Trevor Hastie, & Robert Tibshirani. (2013). An Introduction to Statistical Learning : With Applications in R. Springer.
- Ian Goodfellow and Yoshua Bengio and Aaron Courville. Deep Learning, 2016. URL: http://www.deeplearningbook.org
Recommended Additional Bibliography
- Brooks,Chris. (2019). Introductory Econometrics for Finance. Cambridge University Press. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsrep&AN=edsrep.b.cup.cbooks.9781108422536