2. Machine Learning Models & Algorithms

Label	KSAC Description		Bloom's Taxonomy Level
a	Define key terms of machine learning, including natural language processing.	knowledge	1
b	Explain the difference between classification and regression problems.	knowledge	2
c	Explain the difference between clustering and association problems.	knowledge	2
d	Explain the difference between supervised and unsupervised machine learning, including the limitations of each.	knowledge	2
e	Explain at least three key supervised learning methods. For example, linear/logistic regression, decision trees, random forest, boosted trees, support vector machines, and neural networks.	knowledge	2
f	Explain major deep learning architectures (e.g., multilayer perceptron, feed forward network, CNN, RNN, LSTM, etc.) and when each is best applied.	knowledge	2
g	Explain key unsupervised learning methods, including k-means clustering, hierarchical clustering, association rules, and principal components analysis.	knowledge	2
h	Identify and recognize the clusters of common machine learning applications and limitations.	knowledge	2
i	Identify and recognize use cases to common machine learning applications.	knowledge	2
j	Understand the prerequisites and success criteria for machine learning.	knowledge	2
k	Explain the differences between basic learning algorithms and their applications.	knowledge	2
l	Explain the role and importance of model validation and accuracy metrics in analytics projects, hypothesis testing, and information retrieval.	knowledge	2
m	Explain efforts to make comples machine learning algorithms more explainable, such as Shapley values and/or LIME.	knowledge	2
n	Identify pros and cons of different machine learning algorithms.	knowledge	2
o	Implement, train, and validate a neural network.	skill	3