Understanding Uncertainty in AI: Lecture 25 Overview
This lecture dives into the intricacies of modeling uncertainty in Artificial Intelligence through the use of random variables and Bayesian learning. It covers how prior knowledge can influence model structure and the significance of the EM algorithm in identifying the best model. The lecture also discusses decision-making processes under uncertainty, emphasizing risk minimization. Further reading resources include various conferences and journals focused on Bayesian belief networks and machine learning advancements.
Understanding Uncertainty in AI: Lecture 25 Overview
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Presentation Transcript
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
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
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
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
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
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
