The CIFAR-10 dataset is an image classification dataset with 10 different classes which can be found here: https://www.cs.toronto.edu/~kriz/cifar.html. There are a total of 50000 training images as well as 10000 test images, with a uniform distribution of each class for both training and test data. The code in this repository performs 3 supervised learning algorithms: K Nearest Neighbors, Support Vector Machines, and Neural Networks to classify the images in the CIFAR-10 dataset. The algorithms achieved 31.33%, 58.04%, 59.78% test accuracy respectively.