Last Lecture

1. Last Lecture • Expected Fisher information calculations • Assignments and Term Projects • Due Wednesday 12/18/02 5pm • Office Hours • 12/13/02 Friday 10am till 12pm • 12/15/02 Monday 10am till 12pm • Bringing it all together without focusing on the maths CSI 661 - Uncertainty in A.I. Lecture 25

2. Modelling Uncertainty • Model behavior/situations using random variables • Use prior knowledge to specify the structure between the random variables • Place prior distributions over parameters of random variables • Why is there uncertainty? CSI 661 - Uncertainty in A.I. Lecture 25

3. Learning with Uncertainty • Use Bayes theorem to update our beliefs in these values given the data • Learn parameters • Learn model structure • The EM algorithm can find the “best” model • But there is uncertainty if this is truly the best model • Use Optimal Bayesian Learning to remove this uncertainty CSI 661 - Uncertainty in A.I. Lecture 25

4. Making Decisions • Suppose we find the best model or collection of models • How can we make a decision? • Try to minimize our risk • Minimize maximum risk over many situations • If we were to encounter the same situation again and again, we would minimize our risk. CSI 661 - Uncertainty in A.I. Lecture 25

5. Learning by Compact Encoding • Form of Bayesian learning • P(H,D) = 2-(ML(H)+ML(D|H) • Why? • Compare models of different complexity • Invariant to non-linear data transformations • Consistency • Quantification of Occam’s razor CSI 661 - Uncertainty in A.I. Lecture 25

6. Further Reading • Bayesian belief network • Uncertainty in A.I. Conference • Bayesian Learning • Neural Information Processing (NIPS) Conference • Various Journals • Machine Learning, A.I., A.I. Research, Machine Learning Research, Experimental A.I., Computational Intelligence … • Journal club … CSI 661 - Uncertainty in A.I. Lecture 25