Some sample exam 2 questions: 1. Briefly define the following terms: Unsupervised learning Clustering Algorithm Dendrogram Control Learning Delayed Reward Discounted Future Reward Markov Decision Process Bayes Theorem Maximum a posteriori hypothesis Maximum likelihood hypothesis Bayes optimal classifier Gibbs classifier Bayes network Bagging Boosting Stacking Market Basket Itemset The Apriori Properties Eager learning Lazy Learning Curse of dimensionality kd Tree Single Point Crossover Two Point Crossover Uniform Crossover Point Mutation Inverted Deduction PAC Learning Agnostic Learning e-exhausting a Version Space Shattering a Set of Instances Vapnik-Chervonenkis Dimension 2. What are the two main approaches for generating clusters? Explain in general terms how these approaches work. 3. List two methods that could be used to estimate the validity of the results of a clustering algorithm. Explain how these methods work. 4. Explain how the following clustering methods work: Agglomerative Single Link Agglomerative Complete Link K-Means 5. A distance measure is important both in memory-based reasoning methods such as the k-nearest neighbor method and in clustering. Why is it so critical in these methods. In which is it possible to "learn" to do a better job of measuring the distance between points? Why? 6. Give pseudo-code for the learning cycle of a Q learner. What is the update rule for a deterministic world? How about a non-deterministic world? 7. How are the V(s) and Q(s,a) functions related in Q learning? What are the advantages of using the Q function over the V function? 8. What is Bayes theorem? Discuss two examples showing how Bayes theorem can be used to justify approaches to learning. Also, discuss an example of a learning method based on Bayes theorem. 9. What is a Bayesian Belief network? Give an example of such a network. What are the advantages of a Bayesian network over a naive Bayes learner? 10. Explain the fundamental difference between the Bagging and Ada-Boosting ensemble learning methods? How do these notions relate to the concept of generating a good ensemble? What are the advantages and disadvantages of each method? 11. How does the Apriori algorithm learn an association rule (give the algorithm)? Give two examples of ways to speedup this algorithm. Show an example of how the algorithm works. 12. How does a k-Nearest Neighbor learner make predictions about new data points? How does a distance-weighted k-Nearest Neighbor learner differ from a standard k-Nearest Neighbor learner? What is locally weighted regression? 13. How does a Radial Basis Function network work? How does a kernel function work? 14. How are concepts represented in a genetic algorithm? Give an example of of concept represented in a GA. 15. What operators are used in a genetic algorithm to produce new concepts? Give an example of a mechanism that can be used to judge a GA concept. 16. Give pseudo-code for a general genetic algorithm. Make sure to outline the way concepts are represented, the operators used to create new concepts, how concepts are chosen to reproduce, and how concepts are evaluated. 17. Give two different mechanisms for selecting which members of a GA population should reproduce. What are the advantages and disadvantages of your mechanisms? 18. How does genetic programming work? How is a genetic program defined? What genetic operators can be applied to a genetic program? 19. How does the sequential covering algorithm work to generate a set of rules for a concept? 20. How does FOIL work to generate first-order logic rules for a concept? 21. What does it mean to view induction as inverted deduction? Give a deduction rule and explain how that rule can be inverted to induce new rules.