Data Science
Unsupervised Learning
1 credits
Unsupervised learning. K-means/medoids. Model-based clustering. Expectation-maximization algorithm. Hierarchical clustering. Dimension reduction. Matrix decomposition. Heatmaps, contour plots, dendograms.
Average
90.1%
Students
907
Sections
16
Grade distribution. <50: 0, 50-54: 0, 55-59: 0, 60-63: 1, 64-67: 5, 68-71: 4, 72-75: 13, 76-79: 40, 80-84: 85, 85-89: 194, 90-100: 554.
Historical Averages
2016–2024
Lowest section avg
80.7%
Highest section avg
94.1%
Terms offered
9
Some grade ranges were withheld by UBC to protect student privacy and are not shown above.
Grade data from ubc-pair-grade-data.
| Section | Activity | Status | Time | Instructor |
|---|---|---|---|---|
| 001 | Lecture | Open | Tue 09:30-11:00, Thu 09:30-11:00 | Kehan Sky Sheng |
| 002 | Lecture | Open | Mon 11:00-12:30, Wed 11:00-12:30 | Varada Kolhatkar |
| 003 | Lecture | Open | Tue 09:30-11:00, Thu 09:30-11:00 | Garrett Nicolai |
| L01 | Laboratory | Open | Thu 14:00-16:00 | Kehan Sky Sheng |
| L02 | Laboratory | Open | Tue 14:00-16:00 | Kehan Sky Sheng |
| L03 | Laboratory | Open | Wed 14:00-16:00 | Varada Kolhatkar |
| L04 | Laboratory | Open | Mon 14:00-16:00 | Varada Kolhatkar |
| L05 | Laboratory | Open | Tue 14:00-16:00 | Garrett Nicolai |
| L06 | Laboratory | Open | Tue 16:00-18:00 | Garrett Nicolai |
No course reviews yet. If you took this course, yours would be the first anyone reads.