Mid-Term Exam Study Guide

Plan

The mid-term exam is scheduled in the Testing Center for a Thursday, Friday, and Saturday (see the course schedule on Learning Suite). You will have a 3 hour time limit. The exam is closed book, and you may have no notes. No calculators or digital assistants (you won't need one). I think a well-prepared student will be able to complete the exam in two hours. You’re going to do well!

The format is short answers, worked mathematical solutions, and possibly some T/F.

You will be expected to show your work. You will be graded not only on the correctness of your answer, but also on the clarity with which you express your rationale for your answer; plan to be neat. It is your job to make your understanding clear to us; non-neat work is likely to earn a lower grade. If using a pencil (rather than a pen) helps you be neat, please plan accordingly.

Study

I recommend the following activities to study the topics covered by the exam:

  • Review the lecture notes and identify the topics we emphasized in class. Focus on those listed below.
  • Compare the homework solution keys to your homework assignments, and make sure that you understand the major principles covered in the homework problems.

Topics

The exam will cover a subset of the following topics:

  1. Probability theory: sample spaces, sigma algebras, probability functions
  2. The three axioms of probability
  3. NO proofs involving set theory
  4. Definition of conditional probability
  5. Marginalization, Law of Total Probability
  6. Product rule, chain rule
  7. Independence and conditional independence of events
  8. Random variables
  9. Independence and conditional independence of random variables
  10. Bayes rule
  11. Basic discrete distributions: bernoulli, binomial, categorical, multinomial
  12. Parametric distribution; parameters of distributions
  13. Expected value of a random variable
  14. Querying joint distributions
  15. Efficiency of storage in joint distributions as tables
  16. Rationale for directed grpahical models
  17. Directed graphical models as joint distributions
  18. Visual language of directed graphical models
  19. Reading independence and conditional independence in a directed graphical model
  20. Reading influence / information flow in a directed graphical model
  21. VERY IMPORTANT: Answering questions on directed graphical models: joint queries, marginal queries, conditional queries
  22. Efficiency of answering conditional queries
  23. Text classification
  24. Other kinds of classification problems
  25. “Bag-of-words” assumption
  26. VERY IMPORTANT: Naive Bayes as a directed graphical model, classifying with Naive Bayes, shortcomings of Naive Bayes models
  27. Various event models for Naive Bayes: multivariate bernoulli, multivariate categorical, multinomial (especially multivariate categorical)
  28. Class-conditional language models as classifiers
  29. Evaluating classifiers
  30. Maximum likelihood estimation for the categorical distribution
  31. NO Lagrange Multipliers
  32. The purpose and shapes and parametrization of the Beta distribution
  33. The purpose and shapes and parametrization of the Dirichlet distribution
  34. NO analytical forms of the Beta and Dirichlet distribution
  35. Beta-Binomial conjugacy
  36. Dirichlet-Multinomial conjugacy
  37. NO Completing the integral
  38. Point estimates to summarize the posterior distribution
  39. Maximum a Posteriori (MAP) parameter estimation for the categorical distribution
  40. Relationship between MAP estimation and add-one smoothing
  41. Reading generative stories from a directed graphical model
  42. Plate notation
  43. High-level steps of the Expectation Maximization algorithm
cs-401r/mid-term-topics.txt · Last modified: 2014/10/15 14:20 by ringger
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