hsed / Linear-Models

ICDSS Machine Learning Workshop Series: Linear Models

Workshop #1

• Prerequisites
• Overview
• Agenda
  • Theory
  • Applications
  • Codelab
• Resources
• License

Prerequisites

• Basic Linear Algebra
• Any experience with programming

Overview

The aim of this workshop is to introduce you to Data Science and especially Linear Models. We will answer questions, such as "what is a model?" and "why linear in particular". Then, we will go through some applications, starting with a Simple Beta Hedging algorithm, usually used in Finance. Finally, we will get our hands dirty with implementing this algorithm in vanilla Python and then using off-shelf Machine Learning frameworks, such as scikit-learn and TensorFlow.

Agenda

Theory

Linear Models

Applications

Finance - Simple Beta Hedging

Codelab

Vanilla Python

Setup

1. Follow Python setup environment
2. Run make setup command

Resources

Academia

• Regression Analysis, MIT 18.S096 Topics in Mathematics with Applications in Finance [PDF]
• The Linear Model I, Caltech CS 156 Machine Learning [PDF]
• The Linear Model II, Caltech CS 156 Machine Learning [PDF]
• Linear Regression, Oxford Machine Learning [PDF]

Tutorials

• Python NumPy Tutorial, Stanford CS231n [tutorial]
• Linear Regression Example, scikit-learn [code]
• Linear Regression in TensorFlow, aymericdamien [ipynb]
• Linear Regression, Quantopian [ipynb]
• Multiple Linear Regression, Quantopian [ipynb]
• GradientDescentExample, mattnedrich [Github]

Videos

• Regression Analysis, MIT 18.S096 Topics in Mathematics with Applications in Finance [YouTube]
• The Linear Model I, Caltech CS 156 Machine Learning [YouTube]
• The Linear Model II, Caltech CS 156 Machine Learning [YouTube]
• Linear Regression, Oxford Machine Learning [YouTube]