Artificial Intelligence and Machine Learning in Finance
1st Semester, Course Code: ΜΕΧΤΕ103
Credits: 7,5
Learning Outcomes
This course aims to provide students with a comprehensive understanding of machine learning techniques and their applications in the financial industry. Through a combination of theoretical concepts, practical exercises, and real-world case studies, students will develop the skills necessary to apply machine learning algorithms to financial data, extract insights, and make informed investment and risk management decisions.
General Competences
- Search for, analysis and synthesis of data and information, with the use of the necessary technology
- Adapting to new situations
- Decision-making
- Working independently
- Team work
- Working in an international environment
- Working in an interdisciplinary environment
- Production of new research ideas
- Project planning and management
- Criticism and self-criticism
- Production of free, creative and inductive thinking
Course Content
Lecture 1(Theoretical): Linear Classifiers,Linear and Logistic Regression
Lecture 2(Theoretical): Multilayer Neural Networks
Lecture 3(Theoretical): Recurrent Neural Networks
Lecture 4(Theoretical): Support Vector Machines
Lecture 5 (Theoretical): Reinforcement Learning
Lecture 6 (Laboratory): Standard Machine Learning Models for Stock PricePrediction
Lecture 7(Laboratory): Multi-Layer and Recurrent Neural Networks for Stock Price Prediction
Lecture 8(Laboratory): Machine Learning Models for Investor Risk Toleranceand Robo Advisors
Lecture 9(Laboratory): Machine Learning Algorithms for Automated Trading
Lecture 10(Laboratory): Reinforcement Learning Models for Optimal PortfolioAllocation
Student Performance Evaluation
- Short-Answer Questions 20%
- Open-Ended Questions 10%
- Problem Solving 20%
- Public Presentation 20%
- Final exam 20%
- Computational Assignment 10%
Bibliography
Suggested Bibliography
Goodfellow, I., Bengio, Y., & Courville, A. (2016).Deep learning. MIT press.
Aggarwal, Charu C. ”Neural networks and deep learning.”Springer10.978 (2018): 3.
Haykin, Simon, and N. Network. ”A comprehensive foundation.” Neural networks 2.2004 (2004): 41.
Bishop, Christopher M.Neural networks for pattern recognition. Oxford University Press, 1995.
Theodoridis, Sergios, and Konstantinos Koutroumbas.Pattern recognition. Elsevier, 2006.
Related Academic Journals
Journal of Financial Economics
Journal of Finance
Journal of Financial and Quantitative Analysis
Journal of Banking and Finance
Journal of Financial Markets
Journal of Financial Econometrics
Journal of Empirical Finance
Journal of Portfolio Management