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start [2016/08/29 18:02] – hj | start [2019/08/22 21:28] (current) – hj | ||
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~~NOTOC~~ | ~~NOTOC~~ | ||
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===== Description | ===== Description | ||
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The course introduces some probabilistic models and machine learning methods. The covered topics may include: | The course introduces some probabilistic models and machine learning methods. The covered topics may include: | ||
* Bayesian Decision theory, Generative vs Discriminative modelling | * Bayesian Decision theory, Generative vs Discriminative modelling | ||
- | * Generative Models (1) - multivariate Gaussian, Gaussian mixture model (GMM), | + | * Generative Models (1) - multivariate Gaussian, Gaussian mixture model (GMM), |
* Generative Models (2) - graphical models, directed vs. indirected graphical models, exact inference, approximate inference (loopy belief propagation, | * Generative Models (2) - graphical models, directed vs. indirected graphical models, exact inference, approximate inference (loopy belief propagation, | ||
- | * Discriminative Models (1) - linear discriminant, | + | * Discriminative Models (1) - linear discriminant, |
- | * Discriminative Models (2) - neural networks (NN), back-propagation, | + | * Discriminative Models (2) - neural networks (NN), back-propagation, |
- | * Statistical Modeling Methods - maximum likelihood estimation | + | * Advanced models: hidden Markov model (HMM), |
+ | * Advanced topics: Learnability, | ||
===== Announcements ===== | ===== Announcements ===== | ||
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===== Lecture Times ===== | ===== Lecture Times ===== | ||
- | * Section A: Mondays | + | * Section A: Tuesdays |
===== Lecturer ===== | ===== Lecturer ===== | ||
* Prof. [[http:// | * Prof. [[http:// |
start.1472493732.txt.gz · Last modified: 2016/08/29 18:02 by hj