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Обычная версия сайта
2023/2024

Анализ данных и искусственный интеллект в финансах

Статус: Маго-лего
Когда читается: 1, 2 модуль
Охват аудитории: для своего кампуса
Язык: английский
Кредиты: 6
Контактные часы: 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

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 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

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

Assessment Elements

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

Interim Assessment

  • 2023/2024 2nd 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
Bibliography

Bibliography

Recommended Core Bibliography

  • Artificial intelligence : the basics, Warwick, K., 2012

Recommended Additional Bibliography

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