CSE6328

This is an old revision of the document!

• Week 1: background introduction; speech and spoken language (Weekly Reading: W1)

• Week 2 (updated): Math foundation: probabilities; Bayes theorem; statistics; Entropy; mutual information; decision tree; optimization (Weekly Reading: W2) (A useful online manual on Matrix Calculus)

• Week 3: Bayesian decision theory; Discriminative Models: Linear discriminant functions, support vector machine (SVM); (Weekly Reading: W3_1(part I), W3_2(part II))

• Week 4: Generative Models; model estimation; maximum likelihood, EM algorithm; multivariate Gaussian, Gaussian mixture model, Multinomial, Markov Chain model; (Weekly Reading: W4)

• Week 5: Discriminative Learning; Bayesian Learning; Pattern Verification (Weekly Reading: W5)

• Week 6: Hidden Markov Model (HMM): HMM vs. Markov chains; HMM concepts; Three algorithms (forward-backward, Viterbi decoding, Baum-Welch learning).