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), hidden Markov model (HMM), Multinomial, Markov chain model, n-gram, finite mixture models
• 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, logistic regression, support vector machines (SVM), sparse kernel machines
• Discriminative Models (2) - neural networks (NN), back-propagation, deep learning, recurrent neural networks, convolutional neural networks
• Statistical Modeling Methods - maximum likelihood estimation (MLE), Expectation-Maximization (EM), discriminative training (DT)

• Check the left tab What's New

• Section A: Wednesdays and Fridays, 1:30pm - 3:00pm, FRQ 320 (TBA)

• Prof. Hui Jiang @ CSEB3014