Exam review

Here are some questions you should make sure you know how to answer.

• What is the difference between supervised and unsupervised learning?
• What is the difference between regression and classification?
• How and why would you split your training data?
• What is overfitting? How can you recognize it?
• What is underfitting? How can you recognize it?
• What is regularization? Name three regularized model types.
• What is Bagging?
• What is Boosting?
• What is Stacking?
• Explain how ... works:
  • Logistic Regression
  • Linear Regression
  • k-Nearest Neighbors
  • Decision Tree
  • Random Forest
  • Gradient Descent
  • SVM
  • Kernel methods
  • Neural Networks
  • Convolutional NNs
• Name 3 ...:
  • Kernels
  • Linear models
  • Non-linear models
  • Error measures
  • Neural Network Layers
  • Activation functions
• What is the confusion matrix?
• Explain the difference between accuracy, recall, sensitivity, specificity.