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.