Master
2021/2022
Data Mining and Artificial Intelligence for Finance
Category 'Best Course for Career Development'
Category 'Best Course for Broadening Horizons and Diversity of Knowledge and Skills'
Category 'Best Course for New Knowledge and Skills'
Type:
Elective course (Financial Markets and Institutions)
Area of studies:
Finance and Credit
Delivered by:
Department of Financial Market Infrastructure
Where:
Faculty of Economic Sciences
When:
2 year, 1, 2 module
Mode of studies:
offline
Open to:
students of one campus
Instructors:
Tamara Teplova
Master’s programme:
Financial Markets and Financial Institutions
Language:
English
ECTS credits:
6
Contact hours:
40
Course Syllabus
Abstract
Aims: Applications of data mining / big data and Artificial Intelligence (AI) are highly useful in today's competitive market. In this course, we introduce data mining and AI techniques that can be applied to financial data. For this purpose, several case studies of well-known data mining techniques are used, e.g. risk analysis in banking, insurance / credit card fraud detection, predicting stock market returns, web analytics and social network analysis including Facebook and Twitter text analytics related to finance.
Course Learning Outcomes: Upon successful completion of this course, students will be able to: • Know the advanced techniques of AI and its application to analysis data from financial institutions as well as other decision-making units. • Demonstrate an understanding of the data and resources available on the web of relevance to business intelligence and enable students to access such structured and unstructured data. • Learn advanced data mining and AI methods such as neural networks, clustering, classifications, etc. • Critically analyse the data to real-world problems. • Apply the practical experience and the advanced data mining algorithms needed to reveal patterns and valuable information hidden in large data sets.
Learning Objectives
- The course «Data Mining and AI in Finance» is designed to train specialists in the field of financial data analysis with application of artificial intelligence methods. The aim of the course is to know the advanced techniques of AI and develop skills of data mining for solving problems in the field of risk analysis in banking, insurance /credit card fraud detection, predicting stock market returns, web analytics and social network analysis in finance.
Expected Learning Outcomes
- Know the advanced techniques of AI and its application to analysis data from financial institutions as well as other decision-making units.
- Know the basics of data mining process model for business and management
- Learn advanced data mining and AI methods such as neural networks, clustering, classifications, etc.
- Demonstrate an understanding of the structured and unstructured data and resources available on the web, specially data related to financial institutions that enable students to access and analysis them.
- Critically analyse the data to real-world problems.
- Apply the practical experience and the advanced data mining algorithms needed to reveal patterns and valuable information hidden in large data sets.
Course Contents
- An introduction to data mining process model for business and management
- Advances in neural networks with an applicant to business intelligence
- Use of neural networks in data mining and its application in risk analysis
- Data pre-processing, visualisation and exploratory analysis used in business intelligence
- Data mining predictive models and their applications
- Web-Analytics and data mining models in real-world applications
- Accessing and collecting data from the Web and introduction to text mining
- Classification, decision trees and their applications in Finance
Assessment Elements
- Экзамен по курсу
- Тест №1 по нейронным сетям
- Тест №2 по методам машинного обучения
- Практическое задание по курсу
Interim Assessment
- 2021/2022 2nd module0.3 * Практическое задание по курсу + 0.2 * Тест №2 по методам машинного обучения + 0.2 * Тест №1 по нейронным сетям + 0.3 * Экзамен по курсу
