Bayesian Statistics
Bayesian inference, MCMC, hierarchical models, and applied case studies using PyMC/Stan.
Distributions
11 reviews — mean bin highlighted.Logistics
- Probability
- Statistics
- Machine Learningelective
Reviews (11)
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- 3 / 5Difficulty3/5Workload28 hr/wkRecommendYes
Project-driven course. Most of the 28 hrs/wk go to assignments rather than lectures.
- 3 / 5Difficulty4/5Workload27 hr/wkRecommendYes
Honest review: hard class, heavy workload. Loved solid foundation for follow-on courses. Hated exam format feels punishing.
- 4 / 5Difficulty5/5Workload14 hr/wkRecommendYes
Project-driven course. Most of the 14 hrs/wk go to assignments rather than lectures.
- 4 / 5Difficulty5/5Workload12 hr/wkRecommendYes
Took this in Summer 2024. Workload averaged ~12 hrs/wk. Difficulty felt brutal. Assignments build on each other.
- 3 / 5Difficulty4/5Workload11 hr/wkRecommendYes
Honest review: hard class, light workload. Loved reasonable tooling, no setup hell. Hated vague rubrics on writeups.
- 5 / 5Difficulty3/5Workload18 hr/wkRecommendYes
If you have limited Python exposure, this is approachable. Otherwise plan for ~18 hrs/wk and a few late nights.
- 3 / 5Difficulty4/5Workload23 hr/wkRecommendYes
If you have two prior OMSCS courses, this is approachable. Otherwise plan for ~23 hrs/wk and a few late nights.
- 3 / 5Difficulty4/5Workload16 hr/wkRecommendYes
hard pace, moderate hours weekly. Coming in I had a math-heavy background. Glad I stuck with it.
- 3 / 5Difficulty4/5Workload13 hr/wkRecommendYes
If you have limited Python exposure, this is approachable. Otherwise plan for ~13 hrs/wk and a few late nights.
- 4 / 5Difficulty4/5Workload13 hr/wkRecommendYes
If you have no recent academic study, this is approachable. Otherwise plan for ~13 hrs/wk and a few late nights.
- 4 / 5Difficulty5/5Workload20 hr/wkRecommendYes
Took this in Fall 2023. Workload averaged ~20 hrs/wk. Difficulty felt brutal. Instructors engage in forums.