# 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

### Accordion Content

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

An Introduction to Machine Learning II

### Accordion Content

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

Math Toolbox for Machine Learning

### Accordion Content

• 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 – Regression Methods

### Accordion Content

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

Supervised Learning II

### Accordion Content

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

Decision Trees and Ensemble Models

### Accordion Content

• 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)
• Applications to Finance

Practical Machine Learning Case Studies for Finance

### Accordion Content

• 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

### Message Text

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