Courses

Current Courses:

ECEN 760: Introduction to Probabilistic Graphical Models (Spring 2017)
This course provides a broad overview of various probabilistic graphical models, including Bayesian networks, Markov networks, conditional random fields, and factor graphs. Relevant inference and learning algorithms, as well as their application in various science and engineering problems will be introduced throughout the course.

Textbook:
Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques, The MIT Press

ECEN 644: Advanced Digital Signal Processing  (Spring 2017)
This is a graduate-level advanced digital signal processing (DSP) course that is designed to provide students with a broad perspective on the DSP field.  The course will cover various advanced topics in DSP, including: multirate signal processing, linear prediction and optimum linear filters, adaptive filters, fast Fourier transform (FFT), and power spectrum estimation.


Upcoming Courses:

ECEN 314-502: Signals and Systems (Fall 2017 & Spring 2018)
This is an introductory course on signals and systems. The course will cover: fundamental concepts regarding continuous-time and discrete-time signals and systems; time domain characterization of linear time-invariant (LTI) systems; Fourier analysis; filtering; sampling; modulation techniques for communication systems.

Textbook:
Alan Oppenheim and Alan Willsky, Signals and Systems (2nd edition), Prentice Hall

ECEN 689: Special Topics in Advanced Probabilistic Graphical Models (Spring 2018)
This is a graduate-level course on learning probabilistic graphical models, including Bayesian networks, Markov networks, conditional random fields, and factor graphs. This course will focus on techniques for learning graphical models and their application to various problems across science and engineering. Prerequisite: ECEN760 (or instructor approval)

Textbook:
Daphne Koller and Nir Friedman, Probabilistic Graphical Models: Principles and Techniques, The MIT Press

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