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Structured Output Prediction Course Overview

This advanced course covers binary and multiclass classification, sequence labeling, HMM, CRF, SVM, Perceptron, training regimes, and more. Develop models and algorithms for complex structures using domain knowledge for better predictions.

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Structured Output Prediction Course Overview

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  1. Course Information

  2. Building up structured output prediction • Refresher of binary and multiclass classification • A couple of weeks • Simple structures • Show that multiclass is really a trivial kind of a structure • Training without inference • Sequence labeling problems • HMM, inference, Conditional Random Fields, Structured variants of SVM and Perceptron • Conditional models: How previous algorithms extend to general models • Complexity of inference and inference algorithms • Different training regimes • Training with/without inference • Constraint driven learning, posterior regularization • Learning without full supervision • Latent variables, semi-supervised learning, indirect supervision • Advanced topics in inference

  3. Class focus • To see different examples • Sequence labeling, eg. Part-of-speech tagging • Predicting trees, eg Parsing • More complex structures, eg: relation extraction, object recognition, • And most importantly, Your favorite domain/problem… • To understand underlying concepts • Defining models, training, inference • Using domain knowledge to • Define features • Define models • Make better predictions

  4. The rest of this course • The list may look complicated • You are right to complain • It is meant to be perplexing! • Course objectives • To be able to define models, identify or design training and inference algorithms for a new problem • To be able to critically read current literature • We will revisit these slides in the last lecture!

  5. Course mechanics • Course structure • Lectures by me initially and gradually, presentations by you • No text book • Some useful background reading on course website • Machine learning is a pre-requisite • Assignments(due dates on website/handout, more as we progress) • Three paper reviews (not hand written, please!) • One class presentation • One class project in groups of size 2 • No midterm/final. Instead, project proposal, intermediate checkpoints, and final report and presentation Questions?

  6. What assistance is available for you? • Announcements on the course home page http://svivek.com/teaching/structured-prediction/fall2014 • Lectures • Slides available on website • Additional notes and readings also on website • Email: svivek@cs.utah.edu • Important: Prefix subject with CS6961 • Office hours: • MEB 3126 • Tuesdays 2-3 PM, Thursdays 3:30-4:30 PM • Or by appointment Questions?

  7. Policies (see website/handout for details) • Collaboration vs. Cheating • Collaboration is strongly encouraged, cheating will not be tolerated • The School of Computing policy on academic misconduct • If you haven’t already done this, read and sign the SoC policy acknowledgement form within two weeks • Acknowledge sources and discussions in all deliverables • Late policy • For reviews, a total of 48 hours of credit for the entire semester • Use these hours however you want • Not applicable to project • Access • If you need any assistance, please contact me as soon as possible Questions?

  8. Course expectations This is an advanced course aimed at helping you navigate recent research. I expect you to • Participate in the class • Complete the readings for the lectures • And most importantly, demonstrate independence and mathematical rigor in your work

  9. No readings for next lecture • Please fill out and return the information sheet by the next lecture • For questions about registration, please meet me now Questions?

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