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Kullback–Leibler divergence
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#	Item
151	COMP2610/COMP6261 - Information Theory Lecture 7: Relative Entropy and Mutual Information Mark Reid and Aditya Menon Research School of Computer Science The Australian National University Add to Reading List Source URL: mark.reid.name Language: English - Date: 2015-03-10 00:10:59 Statistics Randomness Science Philosophy of thermal and statistical physics Entropy Mutual information Kullback–Leibler divergence Conditional mutual information Quantities of information Information theory Statistical theory Information
152	Convex Foundations for Generalized MaxEnt Models Rafael Frongillo∗ and Mark D. Reid† ∗ † Add to Reading List Source URL: mark.reid.name Language: English - Date: 2015-03-10 00:10:59 Exponential family Divergence Kullback–Leibler divergence Calculus of variations Entropy Statistics Geometry Bregman divergence
153	Part III Physics exams 2004–2006 Information Theory, Pattern Recognition and Neural Networks Part III Physics exams[removed] Add to Reading List Source URL: www.inference.phy.cam.ac.uk Language: English - Date: 2007-03-08 17:56:25 Mathematics Mutual information Entropy Exponential distribution Conditional entropy Maximum likelihood Noisy-channel coding theorem Kullback–Leibler divergence Z-channel Information theory Statistics Information
154	Information Theory Lecture 3: Applications to Machine Learning NU Logo Use Guidelines Mark Reid Add to Reading List Source URL: mark.reid.name Language: English - Date: 2015-03-10 00:10:59 Science Conditional entropy Entropy Loss function Mutual information Kullback–Leibler divergence Information theory Statistics Information
155	Chap.8 Learning and generalization [Book, Chap. 6] Avoid using a model with too little flexibility to model the underlying nonlinear relation adequately (underfitting ), and using a model with too much flexibility, which Add to Reading List Source URL: www.ocgy.ubc.ca Language: English - Date: 2013-11-04 00:57:35 Maximum likelihood Mean squared error Likelihood function Standard deviation Kullback–Leibler divergence Expectation–maximization algorithm Statistics Estimation theory Normal distribution
156	STAT 538 Homework 1 Out January 14, 2015 Due January 20, 2015 c Marina Meil˘a Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2015-01-15 01:06:00 Mutual information Entropy Normal distribution Kullback–Leibler divergence Multivariate normal distribution Markov chain Conditional independence Independence Conditional mutual information Statistics Information theory Probability and statistics
157	........................................................................... STAT 538 Final Exam Monday March[removed], 10:30-12:20 Student name: .......................................................... Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2015-03-11 22:28:37 Bayesian statistics Information theory Statistical classification Randomness Kullback–Leibler divergence Support vector machine Principle of maximum entropy Entropy Bayesian network Statistics Statistical theory Probability and statistics
158	Similarity Estimation Techniques from Rounding Algorithms Moses S. Charikar Dept. of Computer Science Princeton University 35 Olden Street Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2004-02-04 10:27:32 Information retrieval Hashing Error detection and correction Hash functions Dimension reduction Locality-sensitive hashing Jaccard index Locality preserving hashing Kullback–Leibler divergence Search algorithms Information science Statistics
159	STAT 538 Lecture 8 Maximum Entropy Models c Marina Meil˘a Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2015-02-17 20:52:05 Bayesian statistics Kullback–Leibler divergence Thermodynamics Normal distribution Principle of maximum entropy Convex optimization Expectation–maximization algorithm Entropy Maximum entropy probability distribution Statistics Probability and statistics Statistical theory
160	STAT 538 Lecture 2 Conjugate Function, Bregman Divergences, Concepts of Information Theory c Marina Meil˘a Add to Reading List Source URL: www.stat.washington.edu Language: English - Date: 2015-01-13 22:08:39 Mathematics Statistical theory Randomness Convex analysis Transforms Kullback–Leibler divergence Mutual information Convex conjugate Divergence Statistics Information theory Geometry

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