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ICDSS Machine Learning Workshop Series: Linear Models
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README.md

Linear Models

ICDSS Machine Learning Workshop Series: Linear Models

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.

Tasks

Complete exercise task in 'Demo.ipynb' the 'notebooks' folder. Use the 'Linear Models.ipynb' notebook for guidance

Link to Binder

Binder

Agenda

Theory

Linear Models

Applications

Finance - Simple Beta Hedging

Codelab

Vanilla Python

Setup

macOS
  1. Follow Python setup environment, according to Docs repocitory.
  2. Run source scripts/setup.sh 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]

License

MIT License

Copyright (c) 2017 ICDSS

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

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