AI 221: Classical Machine Learning

COURSE DESCRIPTION

Exploratory Data Analysis. Linear Models. Kernel Methods. Neural Networks. Trees. Clustering. Dimensionality Reduction. Feature Engineering. Density Estimation. Ensemble Learning. Gaussian Processes. Bayesian Methods. Hyperparameter Search. AutoML. Explainability.

COURSE CREDIT

3 units (3 hr/week)

COURSE OBJECTIVES

After completing this course, the students should be able to:

• Describe the major categories of machine learning techniques.
• Demonstrate the understanding of the theory behind common supervised and unsupervised learning algorithms.
• Implement the learning algorithms to problems in science, engineering, and other fields.
• Evaluate data-driven models using common performance metrics, as well as understanding their limitations.

COURSE MATERIALS

Please visit this github repo.