Final Exam Study Guide
Plan
The final exam is scheduled in the classroom on the date scheduled by the University (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 exam is comprehensive. The format is short answers, worked mathematical solutions, and possibly some T/F.
The difficulty level is comparable to the difficulty of the mid-term exam.
You will be expected to show your work. You will be graded not only on the correctness of your answers, but also on the clarity with which you express your rationale for your answers; be neat. It is your job to make your understanding clear to the graders; 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 topics 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.
While you are reviewing the lecture notes, the homework solutions, and the topics in
the mid-term study guide and this final study guide, I
strongly encourage you to build the following lists:
Problems (e.g., classification, clustering)
Models (e.g., Naive Bayes, Gausian Mixture Model)
Algorithms (e.g., the Viterbi algorithm, the Expectation Maximization (EM) algorithm)
Theories (e.g., probability theory)
Methodologies (e.g., feature engineering, unsupervised learning)
Identify common themes and ideas within each of the lists. This will aid you in organizing your thoughts and making comparisons and contrasts.
Topics
The final exam is comprehensive and will cover a subset of the following topics as well as topics from the mid-term exam study guide:
Steps of the Expectation Maximization algorithm
Mixture models
Mixture of multinomials model
NO deriving new EM algorithms for new models
Initialization for Expectation Maximization
Computing the likelihood of the data according to a model
Converting likelihood expressions into log-space
Interpreting Hierarchical Bayesian models
(Multivariate) Gaussian distributions
Gaussian Mixture Models (GMMs)
Sequence labeling
Part-of-speech tagging
Hidden Markov Models (HMMs)
Independence assumptions in HMMs
The Viterbi algorithm
Components of a speech recognition system
Application of HMMs in speech recognition
Application of GMMs in speech recognition
Formulating recognition problems in the source/channel (aka “noisy channel”) paradigm
Language models as Markov chains
Decoding as search
Beam search as an approximation to the Viterbi algorithm
The Monte Carlo principle
Gibbs Sampling
Justifying steps in the derivation of complete conditional distributions for Gibbs sampling
NO novel derivations of complete conditional distributions for Gibbs sampling
Document clustering with Gibbs sampling on a mixture of multinomials
Metrics for clustering
Topic modeling and topic discovery
Latent Dirichlet Allocation (LDA): the generative story and model
Inference in LDA using Gibbs sampling
Strengths and limitations of joint models
Strengths and limitations of conditional models
Answering conditional queries using a joint model versus using a conditional model directly
Maximum entropy classifiers / Logistic regression
NO derivations of gradients of the likelihood (using differential Calculus) for gradient descent / ascent learning of maximum entropy model parameters
The feature engineering cycle
Pros and cons of Naive Bayes versus Maximum entropy as classifiers
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