This page will contain lecture slides, notes, and videos for the course.

**Tentative Schedule:**

**Lecture Slides:**

- Intro
- Intelligent Agents
- Uninformed search
- Optimization (updated 1/29/14)
- Informed search
- Local and adversarial search
- Constraint satisfaction
- Mixed integer programming
- Machine learning (updated 2/19/14)
- Planning 1
- Planning 2
- Bayesian reasoning
- Probabilistic inference
- Planning under uncertainty
- Reinforcement learning
- Computer vision (updated 4/2/14), code
- Scheduling
- Robotics (updated 4/14/14)
- Game Theory

**Supplementary readings:**

- Linear Algebra and Matrix Calculus
- Convex Optimization
- Notes on branch and bound methods (note that these use a bit different notation than the class slides, but the fundamental approaches are the same)
- Presentation of machine learning is quite different than in the textbook. Sources that are closer to the presentation here are "Pattern Recognition and Machine Learning" by Christopher M. Bishop, Chapters 1.1, 3, 4. This material is optional, and the slides and lectures should contain all required material for class and homeworks.
- A good introduction to RRTs is the original page that proposed them, available here. The one difference between this presentation and that from class is that the algorithm there has a SELECT_INPUT method, which finds an input that moves the x_near toward x_new, and then a NEW_STATE routine tha executes this control for some time Delta t. These together are equivalent to the Grow_Towards() function in the slides.
- The material covered in the game theory lecture spans (a very small subset of) Chapters 3 and 4 in this book.

**Lecture Videos:**

- Lecture 1: Intro (slides)
- Lecture 2: Intelligent agents and paradigms for AI (slides)
- Lecture 3: Search (slides)
- Lecture 4: Optimization 1 (slides)
- Lecture 5: Optimization 2 (slides)
- Lecture 6: Informed search (slides)
- Lecture 7: Local search (slides)
- Lecture 8: Constraint satisfaction (slides)
- Lecture 9: Mixed integer programming (slides)
- Lecture 10: Machine Learning 1 (slides)
- Lecture 11: Machine learning 2 (slides)
- Lecture 13: Planning 2 (slides)
- Lecture 14: Probalistic modeling (slides)
- Lecture 15: Probabilistic inference (slides)
- Lecture 16: Planning under uncertainty (slides)
- Lecture 17: Reinforcement learning (slides)
- Lecture 18: Computer vision 1 (slides)
- Lecture 19: Computer vision 2 (slides)
- Lecture 20: Scheduling (slides)
- Lecture 21: Robotics 1 (slides)
- Lecture 22: Robotics 2 (slides)
- Lecture 23: Natural language processing (slides)
- Lecture 25: Computational Game Theory (slides)