Practice Problems

Midterm 01

Practice problems for topics on Midterm 01.

See Sample Midterm 01 for an example midterm from a previous quarter. This is a good way to get a sense of the format of the exam. However, note that the topics covered may change slightly from quarter to quarter, and so the sample exam may not be a perfect match for the topics covered in this quarter.

See all problems in this set, or choose a tag below to view only those problems.

• SVMs
• computing loss
• convexity
• feature maps
• gradient descent
• gradients
• least squares
• linear prediction functions
• linear regression
• nearest neighbor
• object type
• perceptrons
• subgradients

Midterm 02

Practice problems for topics on Midterm 02.

See all problems in this set, or choose a tag below to view only those problems.

• Gaussians
• bayes classifier
• bayes error
• conditional independence
• covariance
• density estimation
• histogram estimators
• kernel ridge regression
• linear and quadratic discriminant analysis
• maximum likelihood
• naive bayes
• regularization

All Tags

All problem types.

See all problems in this set, or choose a tag below to view only those problems.

• Gaussians
• SVMs
• bayes classifier
• bayes error
• computing loss
• conditional independence
• convexity
• covariance
• density estimation
• empirical risk minimization
• feature maps
• gradient descent
• gradients
• histogram estimators
• kernel ridge regression
• least squares
• linear and quadratic discriminant analysis
• linear prediction functions
• linear regression
• maximum likelihood
• maxium likelihood
• naive bayes
• nearest neighbor
• object type
• perceptrons
• regularization
• subgradients