Assignment 4

Support Vector Machines

Published

June 2, 2026

This assignment covers topics from the course on Support Vector Machines. There is only 1 Task, which will make up 100% of the grade. Review the rubric for this assignment here.

Submission Instructions

This assignment is submitted as a .ipynb to Gradescope. Before submitting, make sure you satisfy every item in this checklist:

Your notebook must include your code and all required rendered outputs (printed results, plots, tables). Run all cells before committing and verify all outputs are visible.
The notebooks you submit must contain your solutions: do not submit the blank template notebooks.
After submitting to Gradescope, double-check that all code and outputs are visible.

Resubmissions after the due date that fail to satisfy one of the checks above will be strictly held to the course’s 50%-regrade resubmission policy (see syllabus).

If you have any questions about assignment logistics, please reach out to the instructional team.

Submitting to Gradescope

Once you have finished the assignment, complete the following:

Restart the kernel
Run all cells
Download your .ipynb
Submit your completed .ipynb file to the “Assignemnt 4” portal on Gradescope by May 30th at 11:59 PM.
In Gradescope, view your submission and make sure all outputs are visible.

AI Policy

Expand to read the course AI policy

If you use generative AI on this assignment, you are expected to adhere to the following course policies:

✅ Cultivate understanding: You should be able to fully understand, justify, and explain all the work you submit.
🤔 Question AI outputs: Assume AI-generated answers may be incorrect and verify all information independently.
🚫 Academic integrity: Submitting work you don’t understand or cannot explain will be considered plagiarism, regardless of whether AI use was disclosed.

If there are concerns about AI use in your work, your instructor will ask you to meet and discuss it. If understanding is clearly lacking and this is the first occurrence, you will have the chance to revise and resubmit for 50% of the original maximum grade within two days.

Task 1: Support Vector Machines

Classifying `sklearn`’s `make_moon` dataset with Support Vector Machines

Follow the instructions in lab8.ipynb to complete this task. You will practice:

Preprocessing data with train/test splits and predictor standardization using StandardScaler
Hyperparameter tuning for the following kernels: linear, rbf, and poly
Visualizing decision boundaries
Reviewing model performance with confusion matrices and ROC Curves