Machine Learning in Finance Workshop

This workshop is designed for participants who are new to machine learning and want to acquire skills in this area.  

In order to succeed in finance, individuals need to understand enough about machine learning to use it as a tool. This means that they must, (a) understand in a general way how the algorithms work (b) know how to interpret machine leaning output and be able to make decisions on next steps in an analysis and (c) communicate effectively with data-science professionals. 

The workshop will focus on developing these skills.

Introduction and Unsupervised Learning

• Machine learning vs statistics
• Different learning approaches and their applications
• The use of training, validation, and test data sets
• The variance-bias trade-off
• The k-means algorithm and case study
• Other unsupervised learning tools

Supervised learning

• Linear and logistic regression
• Ridge, lasso, elastic net. Case studies
• Support vector machines
• Decision trees and random forests
• Bagging and boosting; ensemble models
• Neural networks and the gradient descent algorithm

Reinforcement Learning

• Exploration vs exploitation
• Monte Carlo method
• Examples
• Temporal difference learning
• Deep Q-learning
• Applications

NLP and Model Explainability

• Sentiment analysis
• Bag of words models
• Other applications of NLP, ChatGPT
• Importance of explainability
• Shapley values
• LIME