ACE machine learning content

The online curriculum is composed of four modules:

Step 1: What is machine learning?

Download and watch the Introduction video. (Dropbox access is provided after registration).
Complete the Introduction to machine learning quiz.

Answer the questions.

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Questions
Question 1*
ML enables learning directly from data without being explicitly programmed, as opposed to physics-based approaches that require pre-defined rules and models.
Question 2*
Although physics-based approaches are generally reliable, using ML may be needed if the underlying relationships and patterns are unknown.
Question 3*
In supervised learning, the input data does not require the desired solution or labels.
Question 4*
Classification and regression are two types of unsupervised learning.
Question 5*
Supervised machine learning algorithms can be used to predict the label for a new data point.
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Step 2: Foundations of machine learning: Linear models

Download and watch the Linear models video. (Dropbox access is provided after registration).
Complete the Linear models quiz.

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Questions
Question 1*
Linear regression is a supervised learning algorithm.
Question 2*
When using linear regression, the estimated/predicted values for the response variable may follow a quadratic function.
Question 3*
In logistic regression, the estimated/predicted values for the response variable are bounded.
Question 4*
In logistic regression, the model output is the estimated probability that the response variable falls in a certain class.
Question 5*
Logistic regression cannot be used for classification.
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Step 3: Foundations of machine learning: Model evaluation

Download and watch the Model evaluation video. (Dropbox access is provided after registration).
Complete the Model evaluation quiz.

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Questions
Question 1*
A model may be tested objectively by re-applying it to the training data.
Question 2*
In k-fold cross validation, one fold is left out for testing at a time, while all other folds are used for training.
Question 3*
In binary classification, accuracy, sensitivity, and specificity metrics provide some measure of model performance.
Question 4*
In binary classification, mean absolute error and mean squared error are the best metrics to evaluate model performance.
Question 5*
An area under the receiver operating characteristic curve (AUC) that is less than 0.5 indicates a good model performance.
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Step 4: Foundations of machine learning: Non-linear models

Download and watch the Non-linear models video. (Dropbox access is provided after registration).
Complete the Nonlinear models quiz.

Answer the questions.

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Questions
Question 1*
Decision trees are robust against overfitting by design.
Question 2*
Random forest is an ensemble classifier consisting of multiple decision trees.
Question 3*
Despite the term “random” in the name, random forests are completely deterministic.
Question 4*
Feature importance in random forests provides insight into the most important independent variables in the model.
Question 5*
Neural networks are an example of linear models.
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Machine Tool Research Center Department of Mechanical, Aerospace, and Biomedical Engineering