기계학습, 영상처리 기초
Lectures
www.cs.toronto.edu/~mren/teach/csc411_19s/
CSC 411 Winter 2019
Neal, Radford and Hinton, Geoffrey. “A view of the EM algorithm that justifies incremental, sparse, and other variants.” Learning in graphical models. 1999. [pdf]
www.cs.toronto.edu
vision.stanford.edu/teaching/cs131_fall2021/index.html
Computer Vision: Foundations and Applications
Course Description Ever wonder how robots can navigate space and perform duties, how search engines can index billions of images and videos, how algorithms can diagnose medical images for diseases, how self-driving cars can see and drive safely or how inst
vision.stanford.edu
CS229: Machine Learning
CS229: Machine Learning Fall 2020 Instructors Course Description This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-
cs229.stanford.edu
Books
people.eecs.berkeley.edu/~wainwrig/Papers/WaiJor08_FTML.pdf
www.gaussianprocess.org/gpml/chapters/RW.pdf