EE 4250: Machine learning: A Signal Processing Perspective
Credits: 3. Contact Hours: Lecture 2, Discussion 1.
Prereq: EE 3220/STAT 3220 or STAT 3300; and MATH 2070 or MATH 4070/5070
Background material review (probability, calculus, linear algebra), Key machine learning tools and techniques. Supervised Learning: Linear Regression, Logistic Regression, Generative algorithms for classification (Gaussian & discrete-valued case; Naive Bayes assumption), Support Vector Machines, Decision trees; Unsupervised Learning: principal components analysis (PCA), robust PCA, clustering; Introduction to Deep Learning and Neural Networks; Basic Learning Theory and Bias-Variance Tradeoff; introduction to key Bayesian estimation concepts (MMSE estimation, Kalman filter, hidden Markov models).
(Typically Offered: Spring)