1. Topics Covered since 1st midterm…
CSE 391
Fall 2008

2. Probability: Topics to Know
Chain rule
Bayes’ rule
Axioms
Why conditional independence is important and useful
CIS Intro to AI

3. Probability: Skills you should have
Convert a problem to a solvable form using Bayes’ Rule
Given a joint distribution, answer questions about it
Given a situation, build a Bayes network for it with observed, hidden and evidence variables, explaining why you make the independence assumptions you do, and show the CPTs
Determine whether various things are independent or conditionally independent of each other
Apply the Naïve Bayes algorithm to a very small example
Map a Naïve Bayes problem into an appropriate Bayes network
Do a step of the Viterbi algorithm on a very small example
Explain the Forward Algorithm and the Backward Algorithm
CIS Intro to AI

4. Perceptrons and SVMs Topics to Know:
Perceptrons, (averaged perceptrons, voted perceptrons, MIRA) : what they are, and why you might want to use each of them
Understand what the limits of perceptrons are
Understanding “margin”
How linear classifiers can be used to create non-linear classification boundaries
Skills you should have:
Perform a perceptron update step
CIS Intro to AI

5. Logic: Topics to Know Syntax/Semantics distinction
Models and entailment
Soundness and completeness of inference procedures
Syntax and semantics of propositional logic
Validity and satisfiability
(WalkSAT)
Syntax and semantics of first-order logic
Objects, relations and functions in FOL
Common mistakes to avoid with quantifiers from FOL lecture
(Universal and existential instantiation, and the relationship of the resulting knowledge bases to the original)
(Unification)
CIS Intro to AI

6. Logic: Skills you should have
Is proposition P entailed by a knowledge base?
Model-checking by hand
Write out a truth table
Exercises using important rules to know – e.g. de Morgan’s laws
Converting statements into Conjunctive Normal Form
Resolution Refutation
Forward and backward chaining
Representing a problem in propositional (or first-order) logic
(Unification)
CIS Intro to AI