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Course number and title: EE210B Inference over Networks
Credits: 4
Instructor(s)-in-charge: A. H. Sayed (sayed@ee.ucla.edu)
Course type: Lecture
Required or Elective: A Signals and Systems course.
Course Schedule:
Lecture: 4 hrs/week
Outside Study: 8 hrs/week
Office Hours: 2 hrs/week
 
Course Assessment:
Homework: 6 homework assignments
Exams: 1 midterm and 1 final examination
 
Grading Policy: Typically 20% homework, 30% midterm, 50% final.
Course Prerequisites: Prior training in probability theory, random processes, linear algebra, and adaptation is highly recommended. Required EE210A or equivalent or consent of instructor.
Catalog Description: Lecture, four hours; outside study, eight hours. Prior training in probability theory, random processes, linear algebra, and adaptation is highly recommended. Required EE210A or equivalent or consent of instructor. Information processing over networks. Adaptation, learning, estimation and detection over networks. Steepest-descent algorithms, stochastic-gradient algorithms, convergence, stability, tracking, and performance analyses. Distributed optimization. Information flow over networks. Synchronous and asynchronous network behavior. Incremental, consensus, diffusion, and gossip strategies.  
Textbook and any related course material:
Lecture notes by the instructor
 
Course Website
Topics covered in the course:
Incremental, consensus, diffusion, and gossip strategies.
Adaptation and learning over networks.
Optimization over networks.
Inference and detection over networks.
Convergence, stability, and performance studies.
Synchronous and asynchronous network behavior.
Online distributed techniques.
Will this course involve computer assignments? YES Will this course have TA(s) when it is offered? NO

:: Last modified: February 2013 by J. Lin ::

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