Estimation of parameters Maximum Likelihood and Expectation-Maximization, formulation of the objective function of discriminative training, Maximum Mutual information (MMI) criterion, adaptation of GMM models, transforms of features for recognition, modelling of feature space using discriminative sub-spaces, factor analysis, kernel techniques, calibration and fusion of classifiers, applications in recognition of speech, video and text.

State doctoral exam - topics:

1. Maximum Likelihood estimation of parameters of a model
2. Probability distribution from the exponential family and sufficient statistics
3. Linear regression model and its probabilistic interpretation
4. Bayesian models considering the probability distribution (uncertainty) of model parameters
5. Conjugate priors and their significance in Bayesian models
6. Fishers linear discriminant analysis
7. Difference between generative and discriminative classifiers; their pros and cons
8. Perceptron and its learning algorithm as an example of linear classifiers
9. Generative linear classifier - Gaussian classifier with shared covariance matrix
10. Discriminative classifier based on linear logistic regression