EP 09 | CS221 Week 6: Bayesian Learning, Logic & Final Wrap-up

We’ve reached the finish line! 🏁 In our final installment of the Stanford CS221 series, we tackle the last pieces of the AI puzzle: moving from probabilistic reasoning to actual learning, and revisiting the power of symbolic logic. Key Topics: Deep Dive into Bayesian Learning: How we move beyond just using networks to actually learning their parameters and structures from data. Logic Problems: A return to the roots of AI. We explore propositional and first-order logic—how machines represent and reason through complex, rule-based knowledge. Course Conclusion: A bird’s eye view of everything we’ve covered, from Search and ML to Graphical Models and Logic. We connect the dots on what it truly means to build "Principles of AI." Note: This is an AI-generated study resource created via NotebookLM based on Stanford CS221 materials and personal study notes.