CS360 Lab 3: Polynomial Regression

Due: Tuesday, September 29 at 11:59pm

Grace period: will be collected Wednesday at midnight

Overview

Goals:

• To become more familiar with vector and matrix operations in code
• To investigate two different approaches to fitting regression models
• To think about the pros and cons of more flexible models

Note: the instructions for both parts are the linked PDFs below.

Introduction (you are welcome/encouraged to work with a partner)

Begin by working through the small stochastic gradient descent (SGD) example (a continuation from Handout 4) with your partner. Make sure that each you understand the steps and the algorithm (explaining a concept to another person can help solidify your own understanding).

Note: this part does not need to be turned in and will not be graded.

Lab 3 introduction

Lab 03 (individual work from here)

Accept your Lab 3 repository on Github classroom. You should have the following folders/files:

• code: PolynomialRegression.py, run_regression.py
• data : regression_train.csv, regression_test.csv (in the data folder)
• writeup: writeup.tex or README.md (choose one format for your writeup)

Lab 3 instructions