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.

Your contact information
First name*

Enter your first name. For example, Sally or Joe.
Last name*

Enter your last name (surname). For example, Smith.
School or company name*

Enter your school's name. Enter your company name if you are not currently a student.
Email*
Enter Email Confirm Email

Enter your registration email address. For example, [email protected].
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.
Phone
This field is for validation purposes and should be left unchanged.

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.

Answer the questions.

Your contact information
First name*

Enter your first name. For example, Sally or Joe.
Last name*

Enter your last name (surname). For example, Smith.
School or company name*

Enter your school's name. Enter your company name if you are not currently a student.
Email*
Enter Email Confirm Email

Enter your registration email address. For example, [email protected].
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.
Email
This field is for validation purposes and should be left unchanged.

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.

Answer the questions.

Your contact information
First name*

Enter your first name. For example, Sally or Joe.
Last name*

Enter your last name (surname). For example, Smith.
School or company name*

Enter your school's name. Enter your company name if you are not currently a student.
Email*
Enter Email Confirm Email

Enter your registration email address. For example, [email protected].
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.
Phone
This field is for validation purposes and should be left unchanged.

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.

Your contact information
First name*

Enter your first name. For example, Sally or Joe.
Last name*

Enter your last name (surname). For example, Smith.
School or company name*

Enter your school's name. Enter your company name if you are not currently a student.
Email*
Enter Email Confirm Email

Enter your registration email address. For example, [email protected].
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.
Name
This field is for validation purposes and should be left unchanged.

Machine Tool Research Center Department of Mechanical, Aerospace, and Biomedical Engineering