Learning with Marginalized Corrupted Features
Offered By: Center for Language & Speech Processing(CLSP), JHU via YouTube
Course Description
Overview
Explore machine learning techniques for handling finite data through a lecture on learning with marginalized corrupted features. Delve into this alternative approach to regularization and priors, which involves corrupting existing data to generate infinite training samples. Discover how this computationally tractable method leads to fast, generalizable algorithms that scale well to large datasets. Examine applications in risk minimization regularization and marginalized deep learning for document representations. Review experimental results in part of speech tagging, document classification, and image classification. Learn from Kilian Q. Weinberger, an award-winning Assistant Professor from Washington University in St. Louis, known for his research in high-dimensional data analysis, metric learning, and machine-learned web-search ranking.
Syllabus
Learning with Marginalized Corrupted Features - Kilian Weinberger (U of Washington, St. Louis)-2013
Taught by
Center for Language & Speech Processing(CLSP), JHU
Related Courses
Neural Networks for Machine LearningUniversity of Toronto via Coursera 機器學習技法 (Machine Learning Techniques)
National Taiwan University via Coursera Machine Learning Capstone: An Intelligent Application with Deep Learning
University of Washington via Coursera Прикладные задачи анализа данных
Moscow Institute of Physics and Technology via Coursera Leading Ambitious Teaching and Learning
Microsoft via edX