Tuesday

Objectives

• Dive into the Radix Sort algorithm
• Learn about when and why to re-scale data
• Use K-Nearest Neighbors to predict categories by distance
• Become familiar with K-Means Clustering
• Walk through a brief introduction to the scikit-learn Python package

Readings

Sorting

• Radix Sort Visualization

Machine Learning

• Supervised and Unsupervised Machine Learning Algorithms
• Supervised and Unsupervised Learning
• K-Means Clustering
• Feature Scaling and Normalization
• A Detailed Introduction to K-Nearest Neighbors
• K-Nearest Neighbors Algorithm (Wikipedia)
• How KNN Works (video)

Review

None unless asked for.

Concepts

• Rescaling Data
  • Simple Rescaling
  • Orders of Magnitude
• K-Nearest Neighbors Classifier
  • The Algorithm
  • Advantages
  • Disadvantages
  • Attributes
  • Operations
• K-Means Classifier
  • The Algorithm
  • Advantages
  • Disadvantages
  • Attributes
  • Operations

• Introduction to Scikit-Learn
• Radix Sort
• Final Whiteboarding

Assignments

• Django: Add Social Authentication
• Implement the K-Nearest Neighbors Classifier
• Implement the K-Means Classifier
• Implement Radix Sort
• Group Project: Begin User Stories
• Technical Blog Post: Publish Your Post (Due Friday)
• Today I learned...