This course covers essential machine learning algorithms. Topics include supervised learning algorithms (linear and logistic regression, generative models for classification, support vector machines), deep learning algorithms (feedforward neural networks, convolutional neural networks, recurrent neural networks), and unsupervised learning algorithms (mixture models, factor analysis, principal component analysis, latent tree models). Efforts are made to minimize overlaps with CSIT 5210. Consequently, topics such as decision trees, k-nearest neighbor, k-means, ensemble methods and random forests are not included.