Module 4 - Data Science & Machine Learning l

In module four, you will be introduced to the latest data science and machine learning techniques used in finance. Starting with a comprehensive overview of the topic, you will learn essential mathematical tools followed by a deep dive into the topic of supervised learning, including regression methods, k-nearest neighbors, support vector machines, ensemble methods and many more

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An Introduction to Machine Learning l

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  • What is mathematical modeling?
  • Classic modeling
  • How is machine learning different?
  • Principal techniques for Machine Learning

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An Introduction to Machine Learning II

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  • Common Machine Learning Jargon
  • Intro to Supervised Learning techniques
  • Intro to Unsupervised Learning techniques 
  • Intro Reinforcement Learning techniques

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Math Toolbox for Machine Learning

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  • Maximum Likelihood Estimation
  • Cost/Loss Function
  • Gradient Descent
  • Stochastic Gradient Descent
  • Bias and Variance
  • Lagrange Multipliers
  • Principal Component Analysis

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Supervised Learning – Regression Methods

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  • Linear Regression 
  • Penalized Regressions: Lasso, Ridge and Elastic Net 
  • Logistic, Softmax Regression

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Supervised Learning II

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  • K Nearest Neighbors
  • Naïve Bayes Classifier
  • Support Vector Machines

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Decision Trees and Ensemble Models

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  • Entropy minimisation and essential math
  • Splitting process and pruning criteria
  • Random Forests and Extreme Gradient Boosting
  • Bagging with Logit and Decision Tree alternatives (PD Case Study)

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Practical Machine Learning Case Studies for Finance

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  • Macro Forecasting the S&P 500 and the Baa-Spread
  • Sharpe style regression methods for mutual funds
  • Natural Language Processing for Sentiment Analysis of ESG Company Reports

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Lecture order and content may occasionally change due to circumstances beyond our control; however this will never affect the quality of the program.

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Data Science & Machine Learning ll