Courses

CMPE 537

Computer Vision

Graduate computer vision: imaging and cameras, filtering and CNNs, segmentation, features, object and action recognition, motion, tracking, and 3D vision—with implementations and a term project.

CMPE 538

3D Computer Vision

CMPE 544

Pattern Recognition

This course will introduce the fundamentals of statistical pattern recognition with examples from several application areas. Topics include Bayesian decision theory, maximum likelihood and Bayesian parameter estimation, non-parametric techniques, linear discriminant functions, tree methods, multilayer neural networks, bias and variance in regression and classification, resampling for estimating statistics, bagging, boosting, unsupervised learning and clustering.

CMPE 58Z

Introduction to Biometrics

CMPE 593

Deep Learning for Computer Vision

Graduate course on applied deep learning for computer vision: convolutional networks, detection, segmentation, generative models, and vision transformers.

CMPE 597

Deep Learning

An introduction to artificial neural networks, deep learning, fundamental deep architectures, and their applications.