Stanford AA228 Decision Making Under Uncertainty | Autumn 2025 | Bayesian Structure Learning

The lecture provides a solid introduction to Bayesian structure learning, building on previous material on Bayesian networks. The instructor, Amelia Hardy, demonstrates deep familiarity with the subject, having been a student and head TA for the course. The content is well-structured, starting with a review and progressively introducing new concepts. The use of examples, such as the rain/boots/umbrella scenario, helps illustrate abstract ideas. The lecture is technically rigorous, with precise notation and derivations for parameter learning. The discussion of MLE’s limitations and the motivation for Bayesian parameter learning is clear and pedagogically effective. The structure learning section covers key concepts like score functions and search algorithms, but due to time constraints, it may not delve into all theoretical details. The lecture is interactive, with students asking clarifying questions, which enhances understanding. The sources cited, including the course textbook and the instructor’s website, are authoritative. However, the lecture is not a peer-reviewed publication, and some claims rely on the instructor’s expertise. The title accurately reflects the content, and the lecture is suitable for an advanced undergraduate or graduate audience. Overall, the lecture is informative and well-presented, earning a high score for quality and reliability.

The title accurately reflects the content: a lecture on Bayesian structure learning within a decision-making under uncertainty course.

8/10

Lecture from Stanford University's AA228 course, presented by a PhD student with teaching experience. The content is technically rigorous, follows established textbooks, and includes interactive Q&A. However, it is a lecture rather than peer-reviewed research, and some claims rely on the instructor's authority.

Markers derived by PSI from the transcript: the creator did not define chapters.

• 00:00Introduction and agenda
• 02:00Review of Bayesian networks and conditional independence
• 05:00Notation for Bayesian networks: variables, instantiations, parameters
• 10:00Parameter learning: maximum likelihood estimation
• 15:00Limitations of MLE and introduction to Bayesian parameter learning
• 20:00Structure learning: score-based approaches
• 25:00Search algorithms for structure learning
• 30:00Challenges and complexity of structure learning
• 35:00Course project overview and resources

• AA228 Course Website — Course syllabus and materials
• Amelia Hardy's Personal Website — Instructor's academic profile
• AA228 Course Page on Stanford Online — Course enrollment information
• Robotics and Autonomous Systems Graduate Certificate — Related graduate program

• Algorithms for Decision Making — Textbook referenced in the lecture for further reading.
• Probabilistic Graphical Models — Koller and Friedman's comprehensive textbook on graphical models.