1. Different Features

2. Glasses vs. No Glasses

3. Beard vs. No Beard

4. Beard Distinction Ghodsi et, al 2007

5. Glasses Distinction Ghodsi et, al 2007

6. Multiple-Attribute Metric Ghodsi et, al 2007

7. Embedding of sparse music similarity graph Platt, 2004

8. Reinforcement learning Mahadevan and Maggioini, 2005

9. Semi-supervised learning Use graph-based discretization of manifold to infer missing labels. Build classifiers from bottom eigenvectors of graph Laplacian. Belkin & Niyogi, 2004; Zien et al, Eds., 2005

10. correspondences http://www.bushorchimp.com

11. Learning correspondences How can we learn manifold structure that is shared across multiple data sets? c et al, 2003, 2005

12. Mapping and robot localization Bowling, Ghodsi, Wilkinson 2005 Ham, Lin, D.D. 2005

13. Classification

14. Classification

15. Data

16. Features (X) (Green, 6, 4, 4.5) (Green, 7, 4.5, 5) (Red, 6, 3, 3.5) (Red, 4.5, 4, 4.5) (Yellow, 1.5, 8, 2) (Yellow, 1.5, 7, 2.5)

17. Data Representation

19. 11111 10101 11111 10.50.50.51 11111

20. Features and labels (Green, 6, 4, 4.5) (Green, 7, 4.5, 5) (Red, 6, 3, 3.5) (Red, 4.5, 4, 4.5) (Yellow, 1.5, 8, 2) (Yellow, 1.5, 7, 2.5) Green Pepper Red Pepper Hot Pepper

21. Features and labels Objects Features (X)Labels (Y)

22. Classification (New point) (Red, 7, 4, 4.5) h(Red, 7, 4, 4.5) ?

23. Classification (New point) (Red, 5, 3, 4.5) h(Red, 5, 3, 4.5) ?

24. Digit Recognition

26. Classification

27. Classification

28. Classification

29. Classification

30. Computer Vision N. Jojic and B.J. Frey, “ Learning flexible sprites in video layers”, CVPR 2001, (Video)Video

31. Reading Journals: Neural Computation, JMLR, ML, IEEE PAMI Conferences: NIPS, UAI, ICML, AI-STATS, IJCAI, IJCNN Vision: CVPR, ECCV, SIGGRAPH Speech: EuroSpeech, ICSLP, ICASSP Online: citesser, google Books: –Elements of Statistical Learning, Hastie, Tibshirani, Friedman –Learning from Data, Cherkassky, Mulier –Pattern classification, Duda, Hart, Stork –Neural Networks for pattern Recognition, Bishop –Pattern recognition and machine learning, Bishop