Introduction to Machine Learning

Catalog description

. A broad introduction to machine learning and statistical pattern recognition. Topics include fundamental machine learning methods: k-nearest neighbor, decision tree, naive Bayes, artificial neural networks, genetic algorithms, reinforcement learning, support vector machines. Experimental methodologies will be covered including cross-validation, t-test, precision recall and ROC curves.