Course Information
  • Instructor : Krishna R. Narayanan (ude.umat|nrk#ude.umat|nrk)
  • Lecture time: MWF 11.30 - 12.20
  • Office Hours: T, Th 10.30 to 12.00 and by appointment otherwise at 334K Wisenbaker
  • Email : ude.umat|nrk#ude.umat|nrk
  • Recommended Reading :
    • D. Koller and N. Friedman, “Probabilistic Graphical Models : Principles and Techniques”, MIT Press
    • Lecture notes by Prof. Devavrat Shah, MIT
    • C. M. Bishop, "Pattern Recognition and Machine Learning", Springer
    • D. J. C. MacKay, "Information Theory, Inference and Learning", Cambridge University Press
    • M. Mezard and Montanari, "Information, Physics and Computation", Oxford University Press
    • Research papers handed during the course
  • Prerequisites
    • Graduate level understanding of probability (ECEN 646 or equivalent)
    • Programming in a high-level language
    • Exposure to basic concepts in optimization will help. It can be picked up during the course.
    • A willingness to learn
  • Grading policy
    • Homeworks and Projects - 50%
    • Midterm - 25%
    • Final/Project - 25%

Americans with Disabilities Act (ADA) Policy Statement

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