Details about this assignment can be found on the course webpage, under Assignment #1 of Winter 2016.
"This assignment was aimed to practice by 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 than raw pixels (e.g. color histograms, Histogram of Gradient (HOG) features)"[1]