1. CS 6961: Structured Prediction Fall 2014 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 2

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 3

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! 4

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 5 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 6 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 7 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 8

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 9 Questions?