Learning Outcome Statements

a. distinguish between supervised machine learning, unsupervised machine learning, and deep learning;
b. describe overfitting and identify methods of addressing it;
c. describe supervised machine learning algorithms - including penalized regression, support vector machine, k-nearest neighbor, classification and regression tree, ensemble learning, and random forest - and determine the problems for which they are best suited;
d. describe unsupervised machine learning algorithms - including principal components analysis, k-means clustering, and hierarchical clustering - and determine the problems for which they are best suited;
e. describe neural networks, deep learning nets, and reinforcement learning.