Course Information

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?