看到Max Welling教授主页上有不少学习notes，收藏一下吧，其最近出版了一本书呢还，还没看过。
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]