Machine Learning

* Introduction to machine learning
* Supervised and unsupervised learning
* Statistical learning and regression
* Curse of dimensionality and parametric models
* Classification problems, K nearest neighbours
* Simple linear regression and confidence interval
* Multiple Linear Regression and Interpreting Regression Coefficients
* Model Selection and Qualitative Predictors
* Interactions and Nonlinearity
* Introduction to Classification
* Logistic Regression and Maximum Likelihood
* Multivariate Logistic Regression and Confounding
* Linear Discriminant Analysis and Bayes Theorem
* Univariate Linear Discriminant Analysis
* Multivariate Linear Discriminant Analysis and ROC Curves
* Quadratic Discriminant Analysis and Naive Bayes