Regression I

Week 02, Fall 2023

Summary

This week we will begin discussing supervised learning, specifically the regression task. We will look at two methods: k-nearest neighbors, a nonparametric method, and linear regression, a parametric method. We will also introduce the data splitting and overfitting.

Learning Objectives

After completing this week, you are expected to be able to:

  • Identify regression tasks.
  • Use k-nearest neighbors to make predictions for pre-processed data.
  • Use linear regression to make predictions for pre-processed data.
  • Differentiate between parametric and nonparametric regression.
  • Split data into train, validation, and test sets.
  • Avoid overfitting by selecting an a model through the use of a validation set.

Reading

Link Source
Week 02 Concept Scribbles Course Website
Week 02 Notebook [ Rendered Notebook ] Course Website

Video

Head to ClassTranscribe to watch lecture recordings. They are arranged by date in the Lecture Capture Recordings playlist.

Assignments

Assignment Deadline Credit
Lab 01 [ Template ] Thursday, September 7 100%
Homework 01 Thursday, September 7 105%

Office Hours

Staff Day Time Location
David Monday 11:00 AM - 12:00 PM 2328 Siebel Center
David Wednesday 5:00 PM - 6:00 PM Zoom
Lahari Wednesday 4:00 PM - 5:00 PM 0228 Siebel Center (Basement) [ Queue ]
Eunice Thursday 3:00 PM - 4:00 PM 0228 Siebel Center (Basement) [ Queue ]
David Friday 11:00 AM - 12:00 PM 2328 Siebel Center