625-600: Final Exam Review• Final exam: 11/7 (Wed) 12:40pm-1:30pm, in HRBB 126.• Final exam material: slide04 (page 46 and beyond), slide05,slide06 (up to and including page 43).• This is a closed book exam.• You may bring 1 sheet of notes (US letter), however small thefonts may be (obviously it should be legible to you). You may useboth sides.• You may bring a calculator, although you may not need it at all.1Key Points: 1• First-order logic• Prenex normal form, skolemization• Substitution• Unifiers• Resolution2Key Points: 2• Uncertainty• Decision theory example: how prob theory and decision theoryare combined• Probability basics: terminology, notations.• Joint probability distribution: concept• Conditional probability: definition, various ways of representingconditional prob.• Axioms of probability: basic axioms, and using them to provesimple equalities.• Bayes rule: definition and application.3Key Points: 3• Why and when is Bayesian analysis useful?• How to calculate priors from conditional distributions?• How is subjective belief utilized in Bayesian analysis?• What is the role of priors in Bayesian analysis?4Key Points: 4• How is subjective belief utilized in Bayesian analysis?• Bayesian updating: why does that make probabilistic inferenceefficient when multiple evidence comes in?• Belief network: definition, semantics, extracting probabilities ofcertain conjunction of events.5Key Points: 5• Constructing a belief network: what is the procedure? why doesnode ordering matter? how to order the nodes?• Inference in belief networks: what are the kinds of inference?what is the general method? (causal, evidential, etc.)6Key Points: 6• Types of learning• Inductive learning (concept)• Decision tree learning:– What is the embodied principle (or bias)?– How to choose the best attribute? Given a set of examples,choose the best attribute to test first.– What are the issues? noise, overfitting, etc.– Relationship between probability, degree of surprise, degreeof uncertainty, entropy, and information (gain).7Key Points: 7• Neural networks: basics• The central nervous system: how it differs from conventionalcomputers (no need to memorize numbers, anatomical names).• Basic mechanism of synaptic information transfer (no need tomemorize chemicals etc.)• Types of neural networks: two ways of classifying, by feedbackand by topology.• Perceptrons: basic idea, and the geometric interpretation. What isthe limitation? How to