This lecture will generalize LS to weighted LS (WLS) and use WLS to connect with generalized linear models including logistic regression. Remote sensing data for cloud detection will be used.
Creator:
Yu, Bin (University of California, Berkeley)
Created:
2013-06-18
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
This lecture reviews least squares (LS) method for linear fitting and its statistical properites under various linear regression model assumptions. Methods will be illustrated with real data examples from instructor's research projects.
Creator:
Yu, Bin (University of California, Berkeley)
Created:
2013-06-18
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
LS and Maximum Likelihood estimation (MLE) overfit when the dimension of the model is not small relative to the sample size. This happens almost always in high-dimensions. Regularziation often works by adding a penalty to the fitting criterion as in classical model selection methods such as AIC or BIC and L1-penalized LS called Lasso. We will al...
Creator:
Yu, Bin (University of California, Berkeley)
Created:
2013-06-19
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
This lectures will cover two related L2-penalized regularization methods: Ridge Regression and SVM, one from the 40's and one from the 90's. And SVM is one of the two most successful machine learning methods together with Boosting.
Creator:
Yu, Bin (University of California, Berkeley)
Created:
2013-06-24
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In this talk, I'd like to discuss the intertwining importance and connections of three principles of data science in the title in data-driven decisions. The ultimate importance of prediction lies in the fact that future holds the unique and possibly the only purpose of all human activities, in business, education, research, and government alike....
Creator:
Yu, Bin (University of California, Berkeley)
Created:
2016-09-14
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
In recent years network analysis have become the focus of muchresearch in many fields including biology, communication studies, economics, information science, organizational studies, and social psychology. Communities or clusters of highly connected actors form an essential feature in the structure of several empirical networks. Spectral cluste...
Creator:
Yu, Bin (University of California, Berkeley)
Created:
2011-09-26
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.
This lecture will illustrate the power of the sparse coding principle and low-rank regularization in modeling neuron responses to natural images in the very challenging visual cortex area V4.
Creator:
Yu, Bin (University of California, Berkeley)
Created:
2013-06-25
Contributed By:
University of Minnesota, Institute for Mathematics and its Applications.