# CS430 Second Midterm Study Guide

For the second midterm, you are responsible for the following sections
of the textbook:

- Chapter 4.1 (A*)
- Chapter 13 (all)
- Chapter 14.1, 14.2, 14.4
- Chapter 15.1, 15.2, 15.3, 15.5, 15.6
- Chapter 20.1, 20.2, 20.3
- Chapter 22 (all)
- Chapter 23 (all)

## Specific Topics

- You should understand A* very well and be able to simulate it.
- You should understand probability: joint distributions,
conditional probability, independence, conditional independence.
You should expect questions in which you are given a joint
distribution and asked to answer queries from it.
- You should understand Bayesian networks and how they relate the
joint distribution. You should expect questions in which you are
given some evidence and asked to execute the Variable Elimination
Algorithm to infer the marginal probability of some variables.
- You should understand learning for Bayesian networks including
the Laplace correction ("add-one smoothing"). You should expect
questions in which you must compute a conditional probability table
given a set of training examples.
- You should understand the EM algorithm as applied to gaussians
and to ordinary Bayesian networks (i.e., with boolean random
variables).
- You should be able to explain how a speech recognition system
works (what are phones? what is a language model? etc.)
- You should be able to discuss the different phases of natural
language understanding. What kinds of representations are used in
each phase? What kinds of knowledge are needed in each phase? You
should know what a "Speech Act" is and to be able to give some
examples. You should be able to construct a parse tree for a sentence
given a simple context-free grammar and a lexicon. What makes
understanding natural languages hard?
- You should be able to discuss different applications of natural
language processing: information retrieval, information extraction,
and machine translation.