init 2025 assignment1 from 2024

Author: yunfanjiang

assignments/2025/assignment1.md

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+---
+layout: page
+title: Assignment 1
+mathjax: true
+permalink: /assignments2024/assignment1/
+---
+
+<span style="color:red">This assignment is due on **Friday, April 19 2024** at 11:59pm PST.</span>
+
+Starter code containing Colab notebooks can be [downloaded here]({{site.hw_1_colab}}).
+
+- [Setup](#setup)
+- [Goals](#goals)
+- [Q1: k-Nearest Neighbor classifier](#q1-k-nearest-neighbor-classifier)
+- [Q2: Training a Support Vector Machine](#q2-training-a-support-vector-machine)
+- [Q3: Implement a Softmax classifier](#q3-implement-a-softmax-classifier)
+- [Q4: Two-Layer Neural Network](#q4-two-layer-neural-network)
+- [Q5: Higher Level Representations: Image Features](#q5-higher-level-representations-image-features)
+- [Submitting your work](#submitting-your-work)
+
+### Setup
+
+Please familiarize yourself with the recommended workflow by watching the Colab walkthrough tutorial below:
+
+<iframe style="display: block; margin: auto;" width="560" height="315" src="https://www.youtube.com/embed/DsGd2e9JNH4" title="YouTube video player" frameborder="0" allow="accelerometer; autoplay; clipboard-write; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe>
+
+**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](#submitting-your-work).
+
+### 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.
+- Implement and apply a **Two layer neural network** classifier.
+- Understand the differences and tradeoffs between these classifiers.
+- Get a basic understanding of performance improvements from using **higher-level representations** as opposed to raw pixels, e.g. color histograms, Histogram of Oriented Gradient (HOG) features, etc.
+
+### 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.
+
+### Q4: Two-Layer Neural Network
+
+The notebook **two\_layer\_net.ipynb** will walk you through the implementation of a two-layer neural network classifier.
+
+### Q5: Higher Level Representations: Image Features
+
+The notebook **features.ipynb** will examine the improvements gained by using higher-level representations
+as opposed to using raw pixel values.
+
+### 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`.
+* Convert all notebooks into a single PDF file.
+
+If your submission for this step was successful, you should see the following display message:
+
+`### Done! Please submit a1_code_submission.zip and a1_inline_submission.pdf to Gradescope. ###`
+
+**2.** Submit the PDF and the zip file to [Gradescope](https://www.gradescope.com/courses/527613).
+
+Remember to download `a1_code_submission.zip` and `a1_inline_submission.pdf` locally before submitting to Gradescope.