Machine Learning Assignment 2 (May 2019): SVMs, PCA, and Clustering

This document presents a comprehensive solution to a Machine Learning assignment, addressing both Part A and Part B. Part A focuses on Support Vector Machines (SVMs) and Bayes Classifiers, including the implementation and comparison of different kernel functions (linear, polynomial, and Gaussian) for SVMs, along with the application of Bayes' theorem. Part B delves into Principal Component Analysis (PCA) and clustering techniques, specifically K-means clustering. It includes detailed explanations, code implementations (Matlab and Python), and analysis of PCA for dimensionality reduction and visualization, alongside the application of K-means for grouping data points based on similarity. The document provides code snippets, explanations, and results for each part of the assignment, including analysis of error rates, visualization of decision boundaries, and reconstruction of images using PCA. This solution helps students understand and apply core machine learning concepts, providing a valuable resource for learning and assessment.