Classical Machine Learning for Financial Engineering

Week 1: Classical Machine Learning: Overview

• What is Machine Learning (ML) ?

• ML and Finance; not ML for Finance

• Classical Machine Learning: Introduction

• Supervised Learning

• Our first predictor

• Notational conventions

Week 2: Linear regression. Recipe for Machine Learning

• Linear Regression

• The Recipe for Machine Learning

• The Regression Loss Function

• Bias and Variance

Week 3: Transformations, Classification

• Data Transformations: Introduction and mechanics

• Logistic Regression

• Non-numeric variables: text, images

• Multinomial Classification

• The Classification Loss Function

Week 4: Classification continued, Error Analysis

• Baseline model

• The Dummy Variable Trap

• Transformations

• Loss functions: mathematics

Week 5: More Models: Trees, Forests, Naive Bayes

• Entropy, Cross Entropy, KL Divergence

• Decision Trees

• Naive Bayes

• Ensembles

• Feature Importance

Week 6: Support Vector Machines, Gradient Descent, Interpretation

• Support Vector Classifiers

• Gradient Descent

• Interpretation: Linear Models

Week 7: Unsupervised Learning, Dimensionality Reduction

• Unsupervised Learning

• Dimensionality Reduction

• Clustering

• Principal Components

• Pseudo Matrix Factorization: preview of Deep Learning