E1Q2: Training a Support Vector Machine
A Support Vector Machine (SVM) is a discriminative classifier formally defined by a separating hyperplane and are used with associated learning algorithms to analyze data used for classification and regression analysis. Here we will implement and optimize a fully-vectorized loss function and analytic gradient to better classify the images in the CIFAR-10 image set.
E1Q1: k-Nearest Neighbor Classifier
In pattern recognition, the k-nearest neighbors algorithm (k-NN) is a non-parametric method used for classification and regression. In this assignment we utilize a very basic implementation to predict image labels in the CIFAR-10 Dataset.