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David Blei
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#	Item
21	Regularized Regression David M. Blei Columbia University December 3, 2014 Modern regression problems are high dimensional, which means that the number of covariates p is large. In practice statisticians regularize their Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Econometrics Least squares Parametric statistics Regularization Linear regression Tikhonov regularization Elastic net regularization Mean squared error Maximum likelihood Statistics Estimation theory Regression analysis
22	Exact Inference: Elimination and Sum Product (and hidden Markov models) David M. Blei Columbia University Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Graphical models Belief propagation Coding theory Probability theory Tree decomposition Static single assignment form Hidden Markov model Maths24 COMPASS/Sample Code Graph theory Mathematics Compiler construction
23	The Exponential Family David M. Blei Columbia University October 27, 2014 Definition Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Statistical theory Exponentials Bayesian statistics Estimation theory Exponential family Conjugate prior Maximum likelihood Mixture model Normal distribution Statistics Probability and statistics Statistical models
24	Content-based recommendations with Poisson factorization Prem Gopalan, Laurent Charlin, David M. Blei Department of Computer Science Princeton University Princeton, NJ 08540 Add to Reading List Source URL: www.cs.toronto.edu Language: English - Date: 2014-11-03 20:43:16 Expectation–maximization algorithm Poisson distribution Non-negative matrix factorization Recommender system Collaborative filtering Topic model Information retrieval Gamma distribution Latent variable Statistics Statistical natural language processing Latent Dirichlet allocation
25	Graphical Models David M. Blei Columbia University September 17, 2014 These lecture notes follow the ideas in Chapter 2 of An Introduction to Probabilistic Graphical Models by Michael Jordan. In addition, many of the fi Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Graphical model Conditional probability distribution Independence Joint probability distribution Conditioning Hidden Markov model Maths24 Probability theory Statistics Probability
26	Mixed Membership Models David M. Blei Columbia University October 31, 2014 Introduction Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Latent Dirichlet allocation Mixture model Dirichlet distribution Topic model Exponential family Dirichlet process Variational Bayesian methods Statistics Probability and statistics Statistical natural language processing
27	Statistical Models David M. Blei Columbia University October 14, 2014 We have discussed graphical models. Graphical models are a formalism for representing families of probability distributions. They are connected to ef Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Statistical theory Bayesian inference Prior probability Maximum likelihood Normal distribution Maximum a posteriori estimation Expectation–maximization algorithm Mixture model Statistics Bayesian statistics Estimation theory
28	Journal of Machine Learning Research1022 Submitted 2/02; Published 1/03 Latent Dirichlet Allocation David M. Blei Add to Reading List Source URL: www.cs.princeton.edu Language: English - Date: 2010-08-18 22:20:56 Latent Dirichlet allocation Probabilistic latent semantic analysis Mixture model Topic model Dirichlet distribution Expectation–maximization algorithm Multinomial distribution N-gram Maximum likelihood Statistics Probability and statistics Statistical natural language processing
29	Foundations of Graphical Models David M. Blei Columbia University October 14, 2014 Description and Prerequisites Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Machine learning Bayesian statistics Regression analysis Hidden Markov model Gibbs sampling Markov chain Graphical model Mixture model Pattern recognition Statistics Statistical models Markov models
30	Linear regression, Logistic regression, and Generalized Linear Models David M. Blei Columbia University November 18, 2014 Add to Reading List Source URL: www.cs.columbia.edu Language: English - Date: 2015-03-12 00:16:19 Actuarial science Estimation theory Parametric statistics Logistic regression Linear regression Generalized linear model Logistic function Probit Ordinary least squares Statistics Regression analysis Econometrics

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