CSE/STAT 416, Summer 2020: Homework 5: Machine Learning Practice with Kaggle

We recommend that you start this assignment early to make sure you get all the basic setup out of the way first. Using a new course tool (Kaggle in this case) can sometimes be tricky and prone to technical difficulties on your end.

Submission

This assignment is due by Tuesday, July 28 at 11:59 pm.

Note: On Kaggle it lists the close date as two days after the due date listed here. This is intended since Kaggle does not support late submissions. The homework and your submission on Kaggle is due by the due date listed here, but you may use late days and turn it in late.

This assignment is different in that we will also be using Kaggle to evaluate the models you train. You can find the full instructions for assignment on Kaggle (described below). As a reminder from those instrutions, here are the things you should submit and where

  • Your notebook for training the edX student model should be submitted on Gradescope in [A5: Upload] Kaggle Challenge.
  • The model predictions on the test data should be submitted on Kaggle.
  • Your short report should be submitted as part of the programming portion of the assignment on Gradescope in [A5: Programming] Kaggle Challenge.
  • You should complete the concept portion of the assignment on Gradescope in [A5: Concept] Precision/Recall and Retreival.

You may submit any part of the assignment as many times as you want before the late cutoff (remember submitting after the due date will cost late days). Kaggle limits you to submitting 5 times a day (resetting at UTC 00:00) and submitting to it after the due date will count as submitting the assignment late.

Gradescope

Like before, you must submit a notebook that could be run to reproduce your results. Failure to submit the notebook that can train the model will result in a 0 for the programming portion of the assignment.

Kaggle

You can find the link to join our Kaggle competition here. Note that all instructions are on that page.