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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 Case Studies for Machine Learning
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- 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
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Equities & Currencies