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Thought this was cool: Max Welling教授的几个机器学习方法笔记

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Max Welling教授目前在阿姆斯特丹了,之前在UCI,指导的论文获得了ICML2012的Best paper.

S. Ahn, A. Korattikara and M. Welling (2012)
Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring

ICML 2012 [pdf][Google Talk]
Winner of the ICML 2012 Best Paper Award.

看他的主页有很多不错的笔记,分享一下,好好学习:http://www.ics.uci.edu/~welling/classnotes/classnotes.html

Statistical Estimation [ps]
– bayesian estimation
– maximum a posteriori (MAP) estimation
– maximum likelihood (ML) estimation
– Bias/Variance tradeoff & minimum description length (MDL)

Expectation Maximization (EM) Algorithm [ps]
 detailed derivation plus some examples

Supervised Learning (Function Approximation) [ps]
– mixture of experts (MoE)
– cluster weighted modeling (CWM)

Clustering [ps]
– mixture of gaussians (MoG)
– vector quantization (VQ) with k-means.

Linear Models [ps]
– factor analysis (FA)
– probabilistic principal component analysis (PPCA)
– principal component analysis (PCA)

Independent Component Analysis (ICA) [ps]
– noiseless ICA
– noisy ICA
– variational ICA

Mixture of Factor Analysers (MoFA) [ps]
– derivation of learning algorithm

Hidden Markov Models (HMM) [ps]
– viterbi decoding algorithm
– Baum-Welch learning algorithm

Kalman Filters (KF) [ps]
– kalman filter algorithm (very detailed derivation)
– kalman smoother algorithm (very detailed derivation)

Approximate Inference Algorithms [ps]
– variational EM
– laplace approximation
– importance sampling
– rejection sampling
– markov chain monte carlo (MCMC) sampling
– gibbs sampling
– hybrid monte carlo sampling (HMC)

Belief Propagation (BP) [ps]
– Introduction to BP and GBP: powerpoint presentation [ppt]
– converting directed acyclic graphical models (DAG) into junction trees (JT)
– Shafer-Shenoy belief propagation on junction trees
– some examples

Boltzmann Machine (BM) [ps]
– derivation of learning algorithm

Generative Topographic Mapping (GTM) [ps]
– derivation of learning algorithm

Introduction to Kernel Methods: powerpoint presentation [ppt]

Kernel Principal Components Analysis [pdf]

Kernel Canonical Correlation Analysis [pdf]

Kernel Support Vector Machines [pdf]

Kernel Ridge-Regression [pdf]

Kernel Support Vector Regression [pdf]

Convex Optimization [pdf]
A brief introduction based on Stephan Boyd’s book, chapter 5.

Fisher Linear Discriminant Analysis [pdf]

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from 丕子: http://www.zhizhihu.com/html/y2012/4017.html

Written by cwyalpha

十月 16, 2012 在 3:02 下午

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