Chapter 12 Neural Networks: An Application in Finance

Description

This session covers the basics of deep learning, feature learning, feed-forward networks, training neural networks, convolutional neural networks (CNNs) and recurrent neural networks (RNNs). It will also be a session that will focus on the analysis of statistical biases and the ethical issue raised by the use of algorithms.

Concepts discussed :

• 1 applications in international business of neural networks

• 2 I.A. and ethics

Pre-Session Activities/Resources

• LeCun, Yann, Yoshua Bengio, and Geoffrey Hinton. 2015. “Deep Learning.” Nature 521 (7553, 7553): 436-44. https://doi.org/10.1038/nature14539.

• “Google Duplex: An AI System for Accomplishing Real-World Tasks over the Phone.” Google AI Blog. Accessed September 14, 2020. http://ai.googleblog.com/2018/05/duplex-ai-system-for-natural-conversation.html.

• Floridi, Luciano, and Josh Cowls. 2019. “A Unified Framework of Five Principles for AI in Society.” Harvard Data Science Review 1 (1). https://doi.org/10.1162/99608f92.8cd550d1.

Session Activities/Resources

• https://playground.tensorflow.org/

• Spiegelhalter, David. 2020. “Should We Trust Algorithms?” Harvard Data Science Review 2 (1). https://doi.org/10.1162/99608f92.cb91a35a.

Post-Session Activities/Resources

General Resources

• Hidalgo et al. How humans judge machines https://www.judgingmachines.com/
• Viegas, Fernanda https://research.google.com/bigpicture/attacking-discrimination-in-ml/
• PAIR https://pair.withgoogle.com/explorables/measuring-fairness/
• DALEX https://medium.com/responsibleml/basic-xai-with-dalex-part-1-introduction-e68f65fa2889