This course covers the theory and practical algorithms for machine learning from a variety of perspectives. Typical tasks are the classification of data, automatic regression and unsupervised model fitting. Topics covered include: statistical learning methods, shrinkage regression, principle component analysis, decision tree learning, support vector machines, artificial neural network, k-means, k-nearest neighbor, and etc. Short theoretical and programming assignments will be given. Student will also work on a final project of their choice.