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Sebastian Thrun Intro to AI — Stanford CS221 Study Pack
Use this as a lecture map before watching, a review guide between classes, or a timestamped index when you need to revisit one concept. Intro to AI is taught by Sebastian Thrun at Stanford.
Interactive Sebastian Thrun AI course study pack
CS221 covers search, logic, planning, probabilistic reasoning, Bayes nets, HMMs, ML, and robotics — the core topics of classical AI.
Search algorithms including A*, greedy best-first, and adversarial search (minimax) for game playing.
Logic and planning covers propositional logic, first-order logic, and planning algorithms like SATPlan and regression.
Probabilistic reasoning introduces Bayes nets, inference, and the treatment of uncertainty in AI systems.
Hidden Markov Models model temporal probability distributions with hidden states and observable outputs.
Machine learning topics include nearest neighbors, naive Bayes, perceptrons, and the foundations of learning from data.
What this lesson teaches
Stanford CS221 (Introduction to Artificial Intelligence) by Sebastian Thrun covers the foundational topics of AI: search-based problem solving, logical reasoning, probabilistic models, and machine learning. The course provides a broad survey of classical AI techniques, from constraint satisfaction to Bayes nets, before introducing the statistical learning perspective that now dominates the field.
Key concepts and takeaways
- AI problem solving starts with formalizing the task as search in a state space, where a solution is a path from the start to a goal state.
- A* search combines path cost and heuristic estimate to find optimal solutions efficiently when the heuristic is admissible.
- Propositional logic and first-order logic enable agents to reason about facts and derive conclusions from known premises.
- Bayes nets represent joint probability distributions as directed acyclic graphs, enabling efficient inference and learning from data.
- HMMs model sequences of observations generated by hidden states, used in speech recognition and sequence labeling.
- The perceptron is the simplest neural model: a linear threshold function that learns weights from labeled training examples.
Review checklist
Implement A* search for a puzzle (e.g., 8-puzzle) and compare its performance to uniform-cost search and greedy best-first search.
Translate a simple English description into propositional logic and implement a basic theorem prover.
Construct a Bayes net for a simple domain and compute marginal probabilities using variable elimination.
Implement a naive Bayes classifier for text categorization and evaluate accuracy on a labeled corpus.
Lesson chapters
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