Pattern Recognition

PHYS 453 - Pattern Recognition

This repository is the home of example code for PHYS 453.

For tips on editing this or other files, see the Markdown Guide

INSTALLATION

Download the Anaconda platform: https://www.anaconda.com/download/

This contains python and the data science packages we need.

After installation, launch the Anaconda Navigator and open jupyterlab. This opens a new tab in your web browser to run and manage your python scripts.

DOCUMENTATION

Most of what we need can be found at Scikit-learn

My favorite other references are:

• The Whirlwind Tour of Python
• Python Data Science Handbook
• Scikit-learn Presentations
• Machine Learning in Python with scikit-learn tutorials by Data School

OUTLINE

The tutorials are meant to provide all the of examples and links you need

1. Basic introduction to python, numpy, and matplotlib
   • 1-1 Python Basics
   • 1-2 Numpy
   • 1-3 Matplotlibs
2. Generating, loading, and browsing data
   • 2-1 Generating and plotting
   • 2-2 Built-in Datasets
   • 2-3 Loading a CSV file
3. Assessing classifiers
   • 3-1 Metrics and Confusion Matrices
   • 3-2 ROC Curves
4. Decision Trees
   • 4-1 Trees
5. Nearest Neighbors
   • 5 KNN
6. Linear Models
   • 6 SVC, SGD, and LogReg
7. Neural Network
   • 7 Neural Network (MLP) Classifier