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 tools
  • 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 I
  • Maximum Likelihood Estimation
  • Cost/Loss Function
  • Gradient Descent
  • Bias and Variance
  • Lagrange Multipliers
Supervised Learning I – Regression Methods
  • Linear Regression 
  • Penalized Regressions: Lasso, Ridge and Elastic Net 
  • Logistic, Softmax Regression
  • Applications in Finance
Supervised Learning II
  • K Nearest Neighbors
  • Naïve Bayes Classifier
  • Support Vector Machines
  • Applications in Finance
Supervised Learning III
  • Decision trees
  • Ensemble Models – Bagging and Boosting
  • Applications in Finance
Machine Learning Lab I
  • Supervised Learning implementation
  • Python - Scikit Learn
  • Financial Examples & Exercises

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