4.18 Study Note for UW Machine Learning – Regression

Introduction

Learning x à y relationship

Case study: predicting house prices

Module 1: Simple Regression

Gradient descent algorithm

Module 2: Multiple Regression

Incorporate more inputs

Module 3: Assessing Performance

Overfit

Measures of error (training, test, true)

Bias-variance tradeoff

Module 4: Ridge Regression

In addition to measure fit, ask how to choose balance (i.e. model complexity)

Cross validation

Module 5: Feature Selection & Lasso Regression

Efficiency of predictions and interpretability

Lasso total cost = measure of fit + (different) measure of model complexity

Coordinate descent algorithm (very cool, maybe useful in tuning coil)

Module 6: Nearest Neighbor & Kernel Regression

Models, Algorithms, Concepts, very important course

Assumed background: Basic calculus (concept of derivatives), basic linear algebra (vectors, matrices, matrix multiply, important to learn before IBSC program started); programming experience: python; Reliance on GraphLab Create, SFrames, Assignments: use pre-implemented algorithms first, then implement all algorithms from scratch, via Numpy library

Net result: learn how to code methods from scratch

Provided machine in Cloud