Course Description: This is an introductory/medium level course in machine learning, which covers both supervised (regression and classification) and unsupervised learning (clustering and dimension reduction). We will also cover the most popular programming language in statistics and machine learning (R language) and demonstrate how to use R to implement the machine learning algorithms.
Syllabus: review of basic probability and statistics, maximum likelihood and maximum a posteriori. For supervised learning, we will cover linear and polynomial regression, kernel regression, na？ve Bayes classifier, logistic regression, K-nearest neighbor, decision and regression trees, , model selection and regularization methods (ridge and lasso); cross-validation , support-vector machines. Some unsupervised learning methods will also be discussed: principal components and clustering (k-means and hierarchical clustering).