Explore KSACs by Pathway

Explore Knowledge, Skills, Abilities, and Credentials (KSACs) by IT Pathway.

2. Machine Learning Models & Algorithms

Label KSAC Description Bloom's Taxonomy Level Cross-Cutting
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