This course covers various supervised and unsupervised learning algorithms and is intended as a sequel to Machine Learning I. The first half of the course focuses on unsupervised learning with an emphasis on clustering techniques, recommendation systems and dimensionality reduction. In the second half, supervised learning methods will focus on text classification and artificial neural networks. Students are expected to understand the fundamental theories behind these techniques and gain the ability to apply these algorithms to various problems. This is a hands-on course in which students are expected to work on end-to-end machine learning solutions.