slides
Chapter 5
Machine learning
Bayesian Learning & Estimation Theory. Maximum likelihood estimation L = Example: For Gaussian likelihood P(x| ) = N (x|, 2 ), Objective of regression:
Pattern Classification & Decision Theory. How are we doing on the pass sequence? Bayesian regression and estimation enables us to track the man in the.
ReverseTesting: An Efficient Framework to Select Amongst Classifiers under Sample Selection Bias Wei Fan IBM T.J.Watson Ian Davidson SUNY Albany.
CSC321 Introduction to Neural Networks and Machine Learning Lecture 21 Using Boltzmann machines to initialize backpropagation Geoffrey Hinton.
Data Mining: Evaluasi dan Validasi Romi Satria Wahono [email protected] +6281586220090.
December 2011 NIPS Adaptation Workshop With thanks to: Collaborators: Ming-Wei Chang, Michael Connor, Gourab Kundu, Alla Rozovskaya Funding: NSF, MIAS-DHS,
CS910: Foundations of Data Analytics Graham Cormode [email protected] Recommender Systems.
IRE-2014: Sub-topic clustering on tweets and generating brief pseudo summaries
