Pattern recognition theory and practice is concerned with the
design, analysis, and development of methods for the
classification or description of patterns, objects, signals, and
processes. At the heart of this discipline is our ability infer
the statistical behavior of data from limited data sets, and to
assign data to classes based on generalized notions of distances
in a probabilistic space. Many commercial applications of pattern
recognition exist today, including voice recognition (e.g., Amazon
Alexa), fingerprint classification (e.g., MacBook Pro touch bar),
and retinal scanners (e.g., your favorite cheesy sci-fi movie).
Machine learning is a field that is at least 50 years old. Recent advances in deep learning, starting around 2005, have have revolutionized the field. Today, machine learning is one of the most active areas of engineering and is enjoying unprecedented levels of success. However, to understand why these techniques work, we must build a background in traditional pattern recognition and machine learning concepts such as maximum likelihood decision theory, Bayesian estimation, nonparametric methods such as decision trees and support vector machines and temporal modeling approaches such as hidden Markov models. This course is designed to give you a strong background in fundamentals, yet also introduce you to the tools necessary to implement these algorithms.