PHIL 3050 — Information and Uncertainty
4 semester hoursUndergraduateLectureNUpath ADNUpath FQtypical days: M/W/ThBostonTraditional
Introduces the foundations of probabilistic inference, information theory, and their uses for drawing conclusions from noisy data. Applications include diagnosing diseases with inconclusive medical tests, locating autonomous vehicles when sensors are imperfect, and how best to make inferences with incomplete or partial information. Central topics include distinguishing deductive and probabilistic inference, philosophical interpretations of probability, fundamental justifications for the rules of probability, and key concepts of information theory. Introduces analytic and mathematical methods of analysis in these cases and contemporary computational (i.e., programming) techniques for implementing and applying theories of information and probabilistic inference.
Offering history
| Term | Sections | Enrolled | Capacity | Full | Open seats/section |
|---|---|---|---|---|---|
| Spring 2024 | 1 | 19 | 30 | 63% | 11.0 |
| Fall 2024 | 1 | 10 | 15 | 67% | 5.0 |
Snapshots from scheduled scrapes — not live seat availability. "Full" can exceed 100% when sections over-enroll.
Meeting times
Share of recent sections by weekday: M 100% · T 0% · W 100% · Th 100% · F 0%
Common patterns: MWR (100% of sections) — in patterns, R means Thursday
Professors
Fall
- Don Fallis (100% of students) · reviews
Spring
- Don Fallis (100% of students) · reviews
Percentages are each professor's average share of the season's enrolled students in recent terms.
Links
Official catalog (PHIL course descriptions) · Student reviews on RateMyHusky · All PHIL courses · Plan it at numap.app