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ECE 6424 - Probabilistic Graphical Models and Structured Prediction (3C)

Course Description

Advanced concepts in machine learning; focus on probabilistic graphical models and structured output prediction. topics include directed models (Bayes Nets), undirected models (Markov/Conditional Random Fields), exact inference (junction tree), approximate inference (belief propagation, dual decomposition), parameter learning (MLE, MAP< EM, max-margin), structure learning.

Why take this course?

We are witnessing an explosion in data - from billions of images shared online to Petabytes of tweets, medical records and GPS tracks, generated by companies, users and scientific communities. Unfortunately, real-world data are often noisy, ambiguous, and exhibit nontrivial or complex structure. Probabilistic and structured models provide a principled framework for dealing with uncertainty and for converting evidence from multiple nosy sources into a posteriori belief about the world.

Learning Objectives

  • Analyze and contrast board classes of probabilistic graphical models (Bayes Nets vs. Markov Nets; directed vs. undirected graphical models)
  • State and describe independence assumptions encoded by different models
  • Explain and implement algorithms for learning and inference (e.g. belief propagation)
  • Discuss and critique research papers on these topics
  • Identify open research questions in these areas