EDS 232: MACHINE LEARN EDS
University of California, Santa Barbara
Machine learning can help process big/complex data and extract knowledge. I t forms one of the foundations in data science. This course provides a broa d introduction to machine learning and statistical pattern recognition. Top ics include supervised learning (decision tree, random forest, support vect or machines, neural networks) and unsupervised learning (clustering, dimens ionality reduction, deep learning). Problems and exercises are framed withi n environmental science applications. The course uses programming languages like R and Python to support learning how to do advanced scientific progra mming to solve real environmental problems.
Average GPA: 3.80
Grade distribution records: 119 students across 4 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A | 91 | 76.5% |
| A- | 9 | 7.6% |
| B+ | 4 | 3.4% |
| B | 12 | 10.1% |
| B- | 1 | 0.8% |
| C | 1 | 0.8% |
| D | 1 | 0.8% |
Based on 119 student grade records across 4 terms and 2 professors.
Instructors
- Robbins M J 91 students, Average GPA 3.76
- Best B D 28 students, Average GPA 3.93