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EECS 6327 Probabilistic Models & Machine Learning (Fall 2016)
Description
The course introduces some probabilistic models and machine learning methods. The covered topics may include:
- Bayesian Decision theory, Generative vs Discriminative modelling
- Generative Models (1) - multivariate Gaussian, Gaussian mixture model (GMM), Multinomial, Markov chain model, n-gram
- Generative Models (2) - graphical models, directed vs. indirected graphical models, exact inference, approximate inference (loopy belief propagation, variational inference, Monte Carlo methods)
- Discriminative Models (1) - linear discriminant, linear regression, lasso, logistic regression, support vector machines (SVM), sparse kernel machines
- Discriminative Models (2) - neural networks (NN), back-propagation, deep learning, auto-encoder; recurrent neural networks, convolutional neural networks
- Advanced models: hidden Markov model (HMM), Latent Dirichlet Allocation (LDA), Conditional Random Fields (CRF), Convolutional Neural Nets, Recurrent Neural Nets and LSTMs
- Advanced topics: Learnability, Gaussian Processes, Ensemble Methods, Reinforcement Learning, etc.
Announcements
- Check the left tab What's New
Lecture Times
- Section A: Wednesdays and Fridays, 1:30pm - 3:00pm, location (TBA)
Lecturer
- Prof. Hui Jiang @ CSEB3014
start.1472498077.txt.gz · Last modified: 2016/08/29 19:14 by hj