Advanced seminar exploring supervised learning,unsupervised learning,learning theory,reinforcement learning.

Course Description

This course provides a broad introduction to machine learning and statistical pattern recognition.

Topics Covered

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 tradeoffs, practical advice); reinforcement learning and adaptive control. The course also discuss recent applications of machine learning, such as to robotic control, data mining, autonomous navigation, bioinformatics, speech recognition, and text and web data processing.