PHYS 152: Phys& Machine Learning
University of California, Santa Cruz
Review of select topics in statistical physics including information theory, entropy, coupled systems, phase transitions, and symmetry breaking. Introduction to multivariate algorithms, with an emphasis on their foundations in statistical physics and classical mechanics. Notebooks, data preparation, cross-validation, supervised and unsupervised learning. Practical considerations for training and optimizing neural networks and related tools. (Formerly offered as Neural Networks, Statistical Physics and Computing.)
Average GPA: 3.85
Grade distribution records: 80 students across 4 terms.
Grade distribution
| Grade | Students | Percent |
|---|---|---|
| A+ | 36 | 45.0% |
| A | 21 | 26.3% |
| A- | 4 | 5.0% |
| B+ | 2 | 2.5% |
| B | 3 | 3.8% |
| B- | 1 | 1.3% |
| C+ | 1 | 1.3% |
| C | 1 | 1.3% |
| P | 4 | 5.0% |
| NP | 3 | 3.8% |
| S | 3 | 3.8% |
| W | 1 | 1.3% |
Based on 80 student grade records across 4 terms and 1 professor.
Instructors
- Michael Hance 80 students, Average GPA 3.85