Introductory description

In this module, we will demystify Generative AI and illuminate its crucial impact on finance. Students will gain insights into various generative models and hone Python and PyTorch skills. Through our pragmatic approach, students will apply key generative AI concepts like Generative Adversarial Networks, Transformer Models, Diffusion models and Natural Language Processing to real-world scenarios. As we round off the module, we will project into the future of Generative AI and engage in essential discussions on ethical implications. Our primary goal is to empower students with the know-how and aptitude to leverage Generative AI in finance, enhancing decision-making and pioneering innovative strategies.

Module web page

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

Introduction to Generative AI and Its Role in Business
Deep Dive into AI Development
Generative AI Models
Image Generation from Autoencoders to Diffusion Models
Transformer Models and the Attention Mechanism
Forecasting using Transformer Models
Natural Language Processing
AI Agents
Future of Generative AI

Learning outcomes

By the end of the module, students should be able to:

• Demonstrate comprehensive understanding of the fundamental concepts, models, and applications of Generative AI
• Demonstrate comprehensive understanding of and apply various Generative AI models to solve complex problems
• Explore the integration of business data with generative models, understanding how to enhance the quality, reliability, and value of predictions and analyses.
• Demonstrate analytical thinking to leverage the nuanced datasets
• Demonstrate critical evaluation skills by assessing the strengths and limitations of generative AI models

Indicative reading list

Alto, V (2023). Modern Generative AI with ChatGPT and OpenAI Models. O'Reilly
Babcock, J., & Bali, R. (2021). Generative AI with Python and TensorFlow 2: Create images, text, and music with VAEs, GANs, LSTMs, Transformer models. Packt Publishing Ltd.
Coletta, A., Prata, M., Conti, M., Mercanti, E., Bartolini, N., Moulin, A., ... & Balch, T. (2021, November). Towards realistic market simulations: a generative adversarial networks approach. In Proceedings of the Second ACM International Conference on AI in Finance (pp. 1-9).
Foster, D. (2019). Generative Deep Learning: Teaching Machines to Paint. Write, Compose, and Play (Japanese Version) O’Reilly Media Incorporated, 139-140.
Hassan, S (2023). Generative AI 101: Unlocking the Power of Creativity with Machine Learning.
Jang, M., & Lukasiewicz, T. (2023). Consistency analysis of chatgpt. arXiv preprint arXiv:2303.06273.
Lyon, B and Tora, M (2023). Modern Generative AI with ChatGPT and OpenAI Models. Packt Publishing; 1st edition
Wildi, M., & Misheva, B. H. (2022). A Time Series Approach to Explainability for Neural Nets with Applications to Risk-Management and Fraud Detection. arXiv preprint arXiv:2212.02906.
Zhang, L., Wu, T., Lahrichi, S., Salas-Flores, C. G., & Li, J. (2022, August). A Data Science Pipeline for Algorithmic Trading: A Comparative Study of Applications for Finance and Cryptoeconomics. In 2022 IEEE International Conference on Blockchain (Blockchain) (pp. 298-303). IEEE.

Subject specific skills

Master the use of Python for implementing and optimizing Generative AI models
Apply Natural Language Processing (NLP) techniques to analyze and interpret text data
Develop Generative AI solutions using Transformer Models

Transferable skills

Enhanced communication skills
Problem solving skills