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Artificial Intelligence and Machine Learning Basics

Class Code


Class Description

Computers can learn patterns in data that would be nearly impossible to program manually. Through interactive demos, learn techniques that can detect cancer, identify fraud, and interpret images. Prerequisite: Curiosity! No math background required.


Topics include:

Nearest Neighbor

  • Apply nearest neighbor search by hand to classify a data point with numerical features.
  • Explain the weaknesses of nearest neighbor search.


Decision Trees

  • Apply a given decision tree to classify a given data point.
  • Construct a reasonable decision tree by hand given a binary labeled data set.


Logistic Regression

  • Construct a good dividing plane by hand, given labeled data points.
  • Evaluate the goodness of a given dividing plane visually.
  • Given a new data point, use a dividing plane to predict its label.


Gradient Descent

  • Identify the local and global minima of a 2 or 3 dimensional curve.
  • Illustrate the steps gradient descent would take on such a curve to find the local minimum.

University IT Technology Training classes are only available to Stanford University staff, faculty, or students. A valid SUNet ID is needed in order to enroll in a class.