Data Science 561:
Predictive Analytics (4.0 units)
Supervised learning. Linear regression, cross validation, ridge and lasso regression, logistic regression, k-nearest-neighbors, decision trees, random forest and gradient-boosting models, support vector machines, neural networks.
- Crosslist: This course is offered by the ISE department but may qualify for major credit in DSCI. To register, enroll in ISE 529.
| Section | Session | Type | Time | Days | Registered | Instructor | Location | Syllabus | Info |
|---|---|---|---|---|---|---|---|---|---|
| 31529D | 048 | Lecture | 2:00-3:50pm | Mon, Wed | 42 of 45 | Maryam Pishgar | OHE122 |  | |
| 31546D | 048 | Lecture | 2:00-3:50pm | Mon, Wed | 71 of 75 | Cesar Acosta-Mejia | ZHS352 | PDF (305920 KB) | |
| 31726D | 048 | Lecture | 5:00-6:50pm | Mon, Wed | 46 of 90 | Cesar Acosta-Mejia | SLH100 | PDF (306053 KB) | |
| 31729D | 048 | Lecture | 12:00-1:50pm | Mon, Wed | 30 of 40 | Maryam Pishgar | DMC211 | | |
| 31731D | 034 | Lecture | 2:00-3:50pm | Mon, Wed | 8 of 20 | Maryam Pishgar | DEN@Viterbi | |