STAT 217: MIXTURE MODELS AND THEIR APPLICATIONS
University of California, Riverside
4 Units, Lecture, 3 hours; discussion, 1 hour. Prerequisite(s): STAT 170, STAT 171, STAT 201C; or equivalent; graduate standing. An introduction of mixture models (also known as latent class models or unsupervised learning models). Includes expectation-maximization (EM) algorithm, mixtures of regression models, and their applications such as clustering and density estimation.
Average GPA: 3.86
Grade distribution records: 14 students across 1 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 2 | 14.3% |
| A | 8 | 57.1% |
| A- | 2 | 14.3% |
| B+ | 2 | 14.3% |
Based on 14 student grade records across 1 term and 1 professor.
Instructors
- Weixin Yao 14 students, Average GPA 3.86