Machine Learning

Introduction to machine learning, statistical pattern recognition, supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines), unsupervised learning (clustering, dimensionality reduction, kernel methods), learning theory (bias/variance trade-offs, practical advice), reinforcement learning and adaptive control, recent machine learning applications, such as robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.