Project 1

Overview

The objective of this project is to gain experience building a machine learning pipeline that can be used to train and evaluate image classification models. In this project you will implement of set of classification models then apply them to a dataset of images in the context of domestic service robots.

The goals for this project are as follows:

• Implement a K-Nearest Neighbors classifier.
• Implement a Multiclass Support Vector Machine classifier.
• Implement a Softmax classifier.
• Understand the differences and tradeoffs between each of these classifiers.
• Understand the characteristics of instance-level classification using the PROPS dataset.
• Practice with cross validating your machine learning models.

Instructions

1. Download the project starter code
   • Project 1 starter code: P1.zip
2. Unzip the starter code and upload to Google Drive
   • Once unzipped, you should find a root directory titled ‘P1’. The ‘P1’ directory contains all starter code and files needed to complete this project. Please upload the ‘P1’ directory to your Google Drive.
3. Open the *.ipynb and *.py files and implement features

   • We recommend implementing the features in a Google Colab environment. The Colab development environment can be accessed by double-clicking on each *.ipynb and *.py file within your Drive. Instructions for each feature are included in the knn.ipynb and linear_classifier.ipynb files.

   • We suggest starting by implementing the required features as they appear in the knn.ipynb notebook, which can be thought of as part 1 of the project. Then work through the linear_classifier.ipynb notebook as part 2 of the project.

   • While working on the project, keep the following in mind:

     • The notebook and the python file have clearly marked blocks where you are expected to write code. Do not write or modify any code outside of these blocks.
     • Do not add or delete cells from the notebook. You may add new cells to perform scratch computations, but you should delete them before submitting your work.
     • Run all cells, and do not clear out the outputs, before submitting. You will only get credit for code that has been run.
     • To avoid experiencing Colab usage limits, save and close your notebooks once finished working.

4. Submit your implementation for Autograder feedback

   • Once you have implemented a portion of the required features, you may submit your work for feedback from the Autograder. To receive feedback, download your *.ipynb and *.py files then upload them to the Project 1 Autograder. You may submit to the Autograder for feedback up to 5 times per day.
5. Download final implementation
   • After implementing all features, save your work and download the completed *.ipynb and *.py files.
   • The last cell of the linear_classifier.ipynb notebook will generate a internetid_umid_P0.zip file (e.g. kdesingh_1234567_P0.zip).
6. Submit your python and notebook files for grading
   • Upload your files to the Autograder for grading consideration. Your highest score will be used for final grades.

Deadline

This project is due on Monday, Sept 30th at 11:59pm CT. We suggest starting as soon as possible.

Grading

This project will be graded by the Autograder. The project is worth a total of 95 points. You may submit to the Autograder for feedback up to 5 times per day.