Lecture 15 Yuan Alan Qi for midterm questions ‡ Information about paper presentation and course projects ‡ Review of Gaussian process regression ‡ Learning.

 

machine learning Lecture10Qi‡ ZĞǀŝĞǁ ŽĨ ŝƐŚĞƌ͛Ɛ ůŝŶĞĂƌ ĚŝƐĐƌŝŵŝŶĂŶƚ͕ percepton, probabilistic generative models, ‡ Probabilistic discriminative models: Logistic regression.

 

CS 59000 Statistical Machine learning Lecture 11Yuan Alan QiPurdue CSSept. 30 2008.

 

59000 Machine learning Lecture 2.

 

Statistical machine learning Lecture 10 Review of Linear Regression with basis functions Ridge regression and lasso Bayesian model selection.

 

of Laplace approximation, BIC, Bayesian logistic regression ‡ Kernel methods ‡ Kernel ridge regression ‡ Kernel construction ‡ Kernel principle component.

 

CS 59000 Statistical Machine learning Lecture 4Yuan Alan Qi alanqi cs. purdue. edu.

 

Lecture 5 Review of ML estimation and Bayesian treatment of Gaussian and mixture of Gaussians Exponential family Assume 2is known. Given i. i. d. data , the likelihood functionforyis.

 

Lecture 4 Inference step Determine either or. Decision step For given x , determine optimal t.

 

CS 59000 Statistical Machine learning Lecture 1 Yuan Alan Qi alanqi cs. purdue. edu Spring 2011.

 

Lecture 3 ͛ dŚĞŽƌĞŵ posterior v likelihood î prior.

 

ofSVM‡Bayesian networks ‡ Conditional independence, explaining away effect‡D- separation, Markov random fields, Markov blankets, inference on chain Soft.

 

Lecture 8 Review of maximum likelihood estimation, regularized regression, Bayesian regression, and equivalent kernel Bayesian model comparison Linear classification:.

 

Lecture 13 ZĞǀŝĞǁ ŽĨ ŝƐŚĞƌ͛Ɛ ůŝŶĞĂƌ discriminant Peceptron Generative models for classification Conditional classification models: Logistic regression Probit regression.

 

CS 59000 Statistical machine learning Lecture 21 Alan Qi.

 

Lecture 11 Review of Ridge regression and lasso Bayesian model selection Bayesian factor Empirical Bayesian ŝƐŚĞƌ͛Ɛ ůŝŶĞĂƌ discriminant.

 

Statistical Machine learning Lecture 25 Yuan Alan Qi.

 

ofmax-sum algorithm‡K- means clustering ‡K- medoids , Mixture of Gaussians, Vector quantization, Expectation maximization EM , Alternative view ofEM-Sum Algorithm 1 Objective:.

 

CS 59000 Statistical Machine learning Lecture 3Yuan Alan Qi alanqi cs. purdue. edu Sept. 2 2008.

 

for midterm questions ‡ Information about paper review and course projects ‡ Review of Automatic Relevance Determination ‡ GP classification ‡ Support Vector Machine.

 

Lecture 18 PurdueCS‡Review of Gaussian Process Classification ‡ Support Vector Machines for Linearly Vector Machines for Overlapping Class Distributions Likelihood: GP Prior:.

 

Lecture 19 Purdue CS Median: 32 out of 45 points standard deviation: 8. 5.

 

Machine learning of Hidden Markvo Models, forward - backward algorithm, ‡EM for learning HMM parameters, ‡ Viterbi Algorithm, Kalman filtering and smoothing ‡ Rejection.

 

Machine Learning Lecture 14 Yuan Alan Qi‡Review of Gaussian Processes GPs ‡ From linear regression toGP ‡GP for regression ‡ Learning hyperparameters ‡ Automatic.

 

CS   59000   Statistical   Machine   learning Lecture 18:  Midterm   ReviewYuan  Alan  QiPurduce   CS590  ‐‐Yuan Qi   2011.

 

CS 59000 Statistical Machine learning Lecture 25Yuan Alan QiPurdue CSNov. 25 2008.

 

Lecture 8 Review of exponential prior Nonparametric methods Linear Regression with basis functions Family where t is the natural parameterandso g t can be interpreted.

 

D-separation, Markov random fields, Markov blankets ‡ Inference on chain ‡ Inference on chains, factor graphs - separation‡A, B ,and subsets of nodes.

 

Lecture 12 Review of Bayesian factor and empirical Bayesian ŝƐŚĞƌ͛Ɛ ůŝŶĞĂƌ discriminant Peceptron Generative models for classification Suppose we want to compare.

 

CS 59000 Statistical Machine learning Lecture 3Yuan Alan Qi alanqi cs. purdue. edu.

 

! , -. /01 ,1 20/03 41 521 67 8, 0 9/ ! ,-. / , - 0 ,- 1 2 3 4 , 567 8. 99. :, ; 99 , , 99 7 -. /; , 2. ; 0 ,-. / 8. 99. :, ; 99 ;. 99 ;. / 0 ,-. - /- A BCDAEF:-. ,;G BE 0 ,- , 2 3A. ,;G HD.

 

Lecture 1 alanqi cs. purdue. edu Fall 2009 Solve problems in computer vision, natural language processing, systems biology, social network.

 

CS 59000 Statistical Machine learning Lecture 5Alan Qi.

 

CS 59000 Statistical machine learning Lecture 14Alan Qi.

 

CS 59000 Statistical Machine learning Lecture 16Yuan Alan QiPurdue CSOct. 23 2008.

 

CS   59000   Statistical   Machine   learning Lecture 1Yuan   Alan  Qi  alanqi cs. purdue. edu Fall 2008.

 

Lecture13Qi‡ Review of kernel trick, kernel ridge regression and kernel Principle Component Analysis ‡ Gaussian processes GPs ‡ From linear regression.

 

CS 59000 Statistical Machine learning Lecture 10Yuan Alan QiPurdue CSSept. 25 2008.

 

Lecture 9 Review of parzen neighbor classification Linear Regression with basis functions Ridge regression and lasso Bayesian model selection.

 

Machine learning K-medoids , Mixture of Gaussians, ‡ Expectation Maximization EM , Alternative view ofEM‡Hidden Markvo Models, forward - backward algorithm, EM for learning.