1. 08/27: Intro to AI (IntroAI.ppt), Robot Perception, DARPA Grand Challenge 2005 by Sebastian Thrun,

2. 09/10: Perception lecture by David J. Heeger, Intro to Computer Vision, Filters and Edges  

3. 09/17, 24: States and Searching, Robot Path Planning (A* Example)

4. 10/01: Agent Architectures and Hierarchical Control

5. 10/01: Intro to Machine Learning

6. 10/08: Naive Bayes Classifier

7. 10/08: Nearest Neighbor Classifier

8. 10/15: Semi-Supervised Learning and Image Classification

9. 10/22: Random Walk and SSL, and Image Retrieval

10. 10/29: Hurricane Sandy

11. 11/05: PageRank and Markov Process

12. 11/12: Matching with Hungarian Algorithm

13. 11/12: Maximum Weight Subgraph for Matching and Object Detection, Relevant Subgraph

14. 11/19: Clustering and Image Segmentation

15. 11/26: Principal Component Analysis and Face Recognition

16. 11/26: Decision Trees

17. 12/03: Perceptron with SVM introduction

18. 12/10: Final Exam 5:30-7:30 pm

19. SVM and Image Classification

20. Kernel Methods (KernelMethods.pdf)

21. Parameter Estimation (ParameterEstimation.pdf)

22. Ensemble Learning (Ensemble_Learning.ppt)

23. Nonlinear Dimensionality Reduction

24. Markov Random Fields and Stereo Matching and Image Segmentation

25. Markov Chains from Virtual Laboratories in Probability and Statistics:
    Introduction,  Recurrence and Transience, Periodicity, Invariant and Limiting Distributions,
    Time Reversal, Random Walks on Graphs

26. Reinforcement Learning: Markov Decision Processes

27. Probability in AI,  Probabilistic Inference

28. Features and Constraints

29. Feature selection (FSIntroguyon03a.pdf)

Project Assignments

1. A* Shortest path due on Sep. 26, Project1 description, code, evaluation, winners ppt, winning Alg.

2. Semi-Supervised learning due on Nov. 7, Project2 description, evaluation, winning Alg., winners ppt

3. Point matching due on Nov. 29, Project3 description, code, evaluation, winning Alg

4. Clustering

Further Topics in AI

1. Computer Vision 1. (VisOverView.pdf)

2. Computer Vision 2. (MultiView3D.pdf)

3. Matrix Algebra (GreatMatrixIntro.pdf)

Web resources and books:

David Poole and Alan Mackworth, Artificial Intelligence. Foundations of Computational Agents. Cambridge University Press 2010 website

Stephen Marsland, Machine Learning: An Algorithmic Perspective.  CRC 2009

Stanford online course http://www.ai-class.com/

Some slides are based on following courses:

http://www.cs.utah.edu/~piyush/teaching/cs5350.html

http://www.cs.colorado.edu/~grudic/teaching/CSCI3202_2007/index.html