Pattern Recognition and Machine Learning CSE 583/EE 552 (Graduate Level)
Theoretical, Computational and Experimental Regularity on Interdisciplinary, Large Data Sets
Schedule # = 537469
Where: IST 210
When: Wednesdays 2:30-5pm (once per week)
Instructor: Professor Yanxi Liu
This is a course on computational methods for digital data that is across scale, modality and application domains. Our methodology is a unique mixture of theoretical and experimental bases drawn from group theory, pattern theory, statistical learning theory as well as human/animal/insect visual perception research. We aim at automatic pattern discovery, comparison and learning. The students are trained throughout the course to apply theory and algorithms to real world scientific data, with an emphasis on discovering hidden patterns automatically from large data sets, including imagery/video of human faces, urban scenes, zebra in the wild, crowds/cell videos, volumetric images of Zebrafish, C. elegans, neuroradiology images (MR, CT, EEG) and MoCap data of human dance/movements. Your own research data sets are welcome.
A similar course was taught by Professor Liu at Stanford University last winter: http://graphics.stanford.edu/courses/cs468-14-winter/