start
Differences
This shows you the differences between two versions of the page.
| Both sides previous revisionPrevious revisionNext revision | Previous revision | ||
| start [2011/11/16 18:38] – hj | start [2012/01/11 17:07] (current) – hj | ||
|---|---|---|---|
| Line 1: | Line 1: | ||
| ~~NOTOC~~ | ~~NOTOC~~ | ||
| - | ====== | + | ====== |
| ===== Description | ===== Description | ||
| - | The course introduces some basic and important | + | The course introduces some basic statistical |
| * Bayesian Decision theory, Generative vs Discriminative modeling | * Bayesian Decision theory, Generative vs Discriminative modeling | ||
| - | * Generative Models - multivariate Gaussian, Gaussian mixture model (GMM), hidden Markov model (HMM), Markov chain model, n-gram, graphical models | + | * Generative Models - multivariate Gaussian, Gaussian mixture model (GMM), hidden Markov model (HMM), Multinomial, Markov chain model, n-gram, graphical models |
| - | * Discriminative Models - linear discriminant, | + | * Discriminative Models - linear discriminant, |
| * Statistical Modeling Methods - maximum likelihood estimation (MLE), Expectation-Maximization (EM), discriminative training (DT) | * Statistical Modeling Methods - maximum likelihood estimation (MLE), Expectation-Maximization (EM), discriminative training (DT) | ||
| - | * Some Selected | + | * Some selected |
| + | The above methods are equally applicable to other research areas, such as data mining, information retrieval, computer vision, computational linguistics and so on. | ||
| - | The methods are equally applicable to other research areas, such as data mining, information retrieval, computer vision, computational linguistics. | + | ===== Announcements ===== |
| + | * Check the left tab [[: | ||
| ===== Lecture Times ===== | ===== Lecture Times ===== | ||
| - | * Section | + | * Section |
| + | |||
| + | ===== Lecturer ===== | ||
| + | |||
| + | * Prof. [[http:// | ||
start.1321468690.txt.gz · Last modified: by hj
