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

An Introduction to Machine Learning l
  • What is mathematical modeling?
  • Classic modeling
  • How is machine learning different?
  • Principal techniques for Machine Learning
An Introduction to Machine Learning II
  • Common Machine Learning Jargon
  • Intro to Supervised Learning techniques
  • Intro to Unsupervised Learning techniques 
  • Intro Reinforcement Learning techniques
Math Toolbox for Machine Learning
  • Maximum Likelihood Estimation
  • Cost/Loss Function
  • Gradient Descent
  • Stochastic Gradient Descent
  • Bias and Variance
  • Lagrange Multipliers
  • Principal Component Analysis
Supervised Learning – Regression Methods
  • Linear Regression 
  • Penalized Regressions: Lasso, Ridge and Elastic Net 
  • Logistic, Softmax Regression
Supervised Learning II
  • K Nearest Neighbors
  • Naïve Bayes Classifier
  • Support Vector Machines
Decision Trees and Ensemble Models
  • 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)
Practical Case Studies for Machine Learning
  • Sharpe style regression methods for mutual funds and hedge funds
  • How to test for structure in alternative data sets for predictive power in the investment process
  • Interest rate for forecasting using decision trees and ensemble models

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