Apr 28, 2026 | 429 words | 4 min read
7.1. Team Assignments#
The purpose of this assignment is for you to collaboratively implement machine learning models that will classify images based on the features extracted in the previous checkpoint.
Guidelines
This assignment has 4 tasks and is worth a total of 52 points. Tasks 1 to 3 will be assessed during an in-class checkpoint demonstration with a member of the teaching team and using the autograder. Task 4 is a cumulative report graded manually on Gradescope.
In addition to the demonstration, all of your team’s code should be uploaded to Gradescope.
Read each task carefully. You are responsible for following all instructions within each task.
The deliverables table at the bottom of each task lists everything you need to submit. When your work is complete, confirm that all of your deliverables are submitted to Gradescope.
Late submissions will be accepted up to 24 hours after the due date. There is a 25% penalty for late submissions.
Unless mentioned otherwise, for all programming tasks (Python or MATLAB), team and individual, you will be generating a flowchart.
View the Gradescope help for online assignments if you need assistance with submitting your work.
Accessing Gradescope for the first time in ENGR 13300?
Log into Brightspace and open your ENGR 13300 course.
Click Content from the black menu ribbon at the top of the page.
Click Course Links and Resources from the Table of Contents in the left sidebar.
Click the link titled Gradescope.
Select the assignment you are ready to submit.
Opening Gradescope through Brightspace will auto-enroll you in the Gradescope course for your section. You can access Gradescope through Brightspace throughout the semester.
You can resubmit to Gradescope as many times as you need. Only your final submission is graded.
If you cannot copy-paste into Gradescope, then you may need to refresh or update your browser. Use a search engine to find a solution for your specific browser.
Each team member is expected to contribute to every team task.
You and your team members will be held responsible for all material.
All collaborators should be clearly listed, and their contributions are properly referenced.
One team member should upload all deliverables to Gradescope as a single submission.
Be sure to assign all team members to the submission.
For help with submitting team assignments, see Adding Team Members in Gradescope.
Checkpoint Grading Summary
Section |
Item |
Points |
|---|---|---|
Demo |
[In-class] Demonstrate Tasks 1 to 3 and answer TA questions. |
10 |
Task 1 |
[Autograder] Build a K-Nearest Neighbors (KNN) classifier. |
8 |
Task 2 |
[Autograder] Build and train a binary logistic regression model. |
8 |
Task 3 |
[Autograder] Evaluate and visualize KNN and logistic regression performance. |
8 |
Task 4 |
[Gradescope] Finalize the report with all checkpoint content. |
18 |
Total |
52 |