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Regular version of the site

Data Mining and Artificial Intelligence for Finance

2021/2022
Academic Year
ENG
Instruction in English
6
ECTS credits
Course type:
Elective course
When:
2 year, 1, 2 module

Instructor

Course Syllabus

Abstract

Aims: Applications of data mining and Artificial Intelligence (AI) are highly useful in today's competitive market. In this course 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

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

Expected Learning Outcomes

  • Know the basics of data mining process model for business and management
  • 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 AI methods such as neural networks
  • Know the advanced techniques of AI and its application to analysis data from financial institutions as well as other decision-making units.
  • Learn advanced AI methods such as clustering, classifications, etc.
  • Learn advanced data mining and predictive models
  • 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

Course Contents

  • An introduction to data mining process model for business and management
    An introduction to data mining process model for business and management and introduction to Data Mining Package
  • Advances in neural networks with an applicant to business intelligence
    Advances in neural networks with an applicant to business intelligence
  • Use of neural networks in data mining and its application in risk analysis
    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 pre-processing, visualisation and exploratory analysis used in business intelligence
  • Data mining predictive models and their applications
    Data mining predictive models and their applications
  • Web-Analytics and data mining models in real-world applications
    Web-Analytics and data mining models in real-world applications
  • Accessing and collecting data from the Web and introduction to text mining
    Accessing and collecting data from the Web and introduction to text mining
  • Classification, decision trees and their applications in Finance
    Classification, decision trees and their applications in Finance
Assessment Elements

Assessment Elements

  • blocking Экзамен по курсу
  • non-blocking Тест №1 по нейронным сетям
  • non-blocking Тест №2 по методам машинного обучения
  • non-blocking Практическое задание по курсу
Interim Assessment

Interim Assessment

  • Interim assessment (2 module)
    0.3 * Практическое задание по курсу + 0.2 * Тест №1 по нейронным сетям + 0.2 * Тест №2 по методам машинного обучения + 0.3 * Экзамен по курсу
Bibliography

Bibliography

Recommended Core Bibliography

  • Artificial intelligence : the basics, Warwick, K., 2012
  • Artificial intelligence, Davenport, T. H., 2019

Recommended Additional Bibliography

  • Machine learning : beginner's guide to machine learning, data mining, big data, artificial intelligence and neural networks, Trinity, L., 2019