Artifical Intelligence & Machine Learning

Machine Learning

In addition to the challenges we face in acquiring, storing, and transmitting very large amounts of data, we also frequently desire to “learn” from the data in a number of senses.  This arises in many important and emerging signal processing problems when we lack a priori analytical models.  In this case we must learn data models and tune processing algorithms based entirely on training data.  Example applications include:

search engines, medical diagnosis, detecting credit card fraud, stock market analysis, speech and handwriting recognition, object recognition in computer vision, spam filtering. 

We are exploring a wide range of machine learning algorithms to that aid in tasks including data visualization and exploration, dimensionality reduction, nonlinear regression, and pattern classification.

Courses

CEVE 496    SYSTEM I.D. & MACHINE LEARNING
CEVE 596    SYSTEM I.D. & MACHINE LEARNING
COMP 441    LARGE-SCALE MACHINE LEARNING
COMP 502    NEURAL MACHINE LEARNING I
COMP 540    STATISTICAL MACHINE LEARNING
COMP 542    LARGE-SCALE MACHINE LEARNING
COMP 602    NEURAL MACHINE LEARNING II
COMP 640    GR SEM IN MACHINE LEARNING
COMP 642    MACHINE LEARNING
DSCI 303    MACHINE LEARNING FOR DS
ELEC 478    INTRO TO MACHINE LEARNING
ELEC 502    NEURAL MACHINE LEARNING I
ELEC 515    EMBEDDED MACHINE LEARNING
ELEC 578    INTRO TO MACHINE LEARNING
ELEC 602    NEURAL MACHINE LEARNING II
ELEC 681    FUNDAMENTALS MACHINE LEARNING
STAT 413    INTRO TO STAT MACHINE LEARNING
STAT 502    NEURAL MACHINE LEARNING I
STAT 602    NEURAL MACHINE LEARNING II
STAT 613    STAT MACHINE LEARNING

Centers and Institutions

K2I
RUCCAM

Research Groups