This assignment is due on April 16 at 11:59pm PST.

Starter code containing Colab notebooks can be downloaded here.

• Setup
• Goals
• Q1: k-Nearest Neighbor classifier
• Q2: Training a Support Vector Machine
• Q3: Implement a Softmax classifier
• Submitting your work

Setup

Note. Ensure you are periodically saving your notebook (File -> Save) so that you don't lose your progress if you step away from the assignment and the Colab VM disconnects.

Once you have completed all Colab notebooks except collect_submission.ipynb, proceed to the submission instructions.

Goals

In this assignment you will practice putting together a simple image classification pipeline based on the k-Nearest Neighbor or the SVM/Softmax classifier. The goals of this assignment are as follows:

• Understand the basic Image Classification pipeline and the data-driven approach (train/predict stages).
• Understand the train/val/test splits and the use of validation data for hyperparameter tuning.
• Develop proficiency in writing efficient vectorized code with numpy.
• Implement and apply a k-Nearest Neighbor (kNN) classifier.
• Implement and apply a Multiclass Support Vector Machine (SVM) classifier.
• Implement and apply a Softmax classifier.

Q1: k-Nearest Neighbor classifier

The notebook knn.ipynb will walk you through implementing the kNN classifier.

Q2: Training a Support Vector Machine

The notebook svm.ipynb will walk you through implementing the SVM classifier.

Q3: Implement a Softmax classifier

The notebook softmax.ipynb will walk you through implementing the Softmax classifier.

Submitting your work

Important. Please make sure that the submitted notebooks have been run and the cell outputs are visible.

Once you have completed all notebooks and filled out the necessary code, you need to follow the below instructions to submit your work:

1. Open collect_submission.ipynb in Colab and execute the notebook cells.

This notebook/script will generate a zip file of your code (.py and .ipynb) called a1_code_submission.zip.

If your submission for this step was successful, you should see the following display message:

### Done! Please submit a1_code_submission.zip to Gradescope. ###

2. Submit the zip file to Gradescope.

Remember to download a1_code_submission.zip locally before submitting to Gradescope.

3. Ensure that you have answered the inline questions scattered throughout the notebooks on Gradescope.