Welcome to CS1420!
This course explores the theory and practice of statistical machine learning, focusing on computational methods for supervised and unsupervised data analysis. Specific topics include Bayesian and maximum likelihood parameter estimation, regularization and sparsity-promoting priors, kernel methods, the expectation maximization algorithm, and models for data with temporal or hierarchical structure. Applications to regression, categorization, clustering, and dimensionality reduction problems are illustrated by examples from vision, language, bioinformatics, and information retrieval.
- Fill out the Collaboration Policy Form. You will not be able to hand in assignments without doing so.
- Set up your iClicker/REEF account before Tuesday's class. Instructions available here .
- Sign up for Piazza. Please use Piazza for all questions about homeworks and course material.
- Homework 6 is out!