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Learning Outcome Statements PDF Download
|1. What is machine learning?|
a. distinguish between supervised machine learning, unsupervised machine learning, and deep learning;
|2. Overview of evaluating ML algorithm performance|
b. describe overfitting and identify methods of addressing it;
|3. Supervised machine learning algorithms|
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;
|4. Unsupervised machine learning algorithms|
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;
|5. Neural networks, deep learning nets, and reinforcement learning|
e. describe neural networks, deep learning nets, and reinforcement learning.