In this Machine learning course, you will learn techniques to extract features from data so as to solve certain tasks, such as speech recognition, machine translation, medical diagnosis and prognosis, automatic algorithm configuration, robot control, and much more. This course provides a detailed information about machine learning and statistical pattern recognition. You will become familiar with 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. You will get knowledge of recent applications of machine learning, such as to speech recognition, robotic control, data mining, and many more.
