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CS 6961: Structured PredictionFall 2014
Course Information
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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
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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
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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!
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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 presentationQuestions?
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What assistance is available for you?
• Announcements on the course home pagehttp://svivek.com/teaching/structured-prediction/fall2014
• Lectures– Slides available on website– Additional notes and readings also on website
• Email: [email protected]– Important: Prefix subject with CS6961
• Office hours:– MEB 3126– Tuesdays 2-3 PM, Thursdays 3:30-4:30 PM– Or by appointmentQuestions?
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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?
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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
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• 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?