Machine Learning

Catalog description

This course provides a broad introduction to machine learning and statistical pattern recognition including supervised, unsupervised, and ensemble learning. Topics include K-NN, Naïve Bayes Classifier, parametric and non-parametric methods, support vector machines, kernel machines, neural networks, clustering, dimensionality reduction, and model evaluation. The learning theory including bias/variance tradeoffs and large margins will be introduced. This course will also discuss recent applications of machine learning such as data mining, autonomous navigation, speech recognition, and text and web data processing.