The course introduces some basic and important modeling methods in pattern recognition and machine learning and their applications to speech and language processing applications. The covered topics include:

• Bayesian Decision theory
• Generative Models - multivariate Gaussian, Gaussian mixture model (GMM), hidden Markov model (HMM), Markov chain model, n-gram, graphical models
• Discriminative Models - linear discriminant, logistic regression, support vector machines(SVM), neural networks, sparse kernel machines
• Statistical Modeling Methods - maximum likelihood estimation (MLE), Expectation-Maximization (EM), discriminative training (DT)
• Some Selected applications - speech recognition, text categorization, machine translation, spoken language processing

The methods are equally applicable to other research areas, such as data mining, information retrieval, computer vision, computational linguistics.

• Section A: Mondays and Fridays, 11:00am - 12:00pm, CSE 111