The aim of this course is to introduce students to the methods and algorithms of machine learning and in particular deep learning models for supervised and unsupervised type learning. Topics covered are: neural networks models for classifications and clustering problems, linear and logistic regression, support vector machines (SVM), probabilistic models, dimensionality reduction techniques, reinforced learning, ensemble learning, multiclass classifications, and model selection and evaluation. Students are also required to work on an individual project that embodies a machine learning solution to a problem.