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
  • Learning Theory: The bias-variance problem
  • Linear Algebra for ML
  • Empirical Risk minimization
  • Gradient descent (stochastic and accelerated)
  • Constrained optimization and its applications
  • Probabilistic Modelling and Inference
  • Gaussian Processes 
  • The art and theory of model selection
Supervised Learning I
  • 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
  • Introduction to decision trees, basic definitions
  • CART: Classification and Regression Trees
  • Measuring the performance of trees (entropy, Gini impurity) 
  • Fitting decision trees to data
  • The bias and variance trade-off for decision trees
  • Bootstrap Aggregating (Bagging) for variance reduction
  • Random Forests
  • Boosting for bias reduction
  • Generic Boosting (Anyboost)
  • Gradient Boosted Regression Trees
  • Adaptive Boosting (AdaBoost)
  • Applications to Finance
     
Practical Machine Learning Case Studies for Finance
  • 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

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