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 a 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.
  • Implement the forward and backward pass for a two layer neural network.
  • Understand the differences and tradeoffs between each of these classifiers.
  • Understand the characteristics of instance-level classification using the PROPS Classification Dataset.
  • Practice with cross validating your machine learning models.

Instructions

  1. Download the project starter code
  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. Then work through the two_layer_net.ipynb notebook as the final part 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 2 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 uniqueid_umid_P1.zip file. The zip file should include knn.ipynb, knn.py, linear_classifier.ipynb, linear_classifier.py, svm_best_model.pt, softmax_best_model.pt, two_layer_net.ipynb, two_layer_net.py, and nn_best_model.pt for this assignment.
  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 Thursday, February 1st at 11:59pm EST. We suggest starting as soon as possible.

Grading

This project will be graded by the Autograder. The project is worth a total of 130 points. You may submit to the Autograder for feedback up to 3 times per day.