Theory and practice of statistical prediction. Contemporary methods as extensions of classical methods. Topics: optimal prediction rules, the curse of dimensionality, empirical risk, linear regression and classification, basis expansions, regularization, splines, the bootstrap, model selection, classification and regression trees, boosting, support vector machines. Computational efficiency versus predictive performance. Emphasis on experience with real data and assessing statistical assumptions.

Common nonparametric tests such as the sign, Wilcoxon, Kruskal-Wallis and rank correlation tests, and joint estimates and confidence intervals derived from these tests. Exact and asymptotic distribution theory, both in randomization and population models.

Common nonparametric tests such as the sign, Wilcoxon, Kruskal-Wallis and rank correlation tests, and joint estimates and confidence intervals derived from these tests. Exact and asymptotic distribution theory, both in randomization and population models.

Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.

Knowledge and knowledge-representation languages; Prolog and logic programming; query languages and relational models of data. Question answering, inference, and information analysis.

Syntax and semantics of synthetic (formal and programming) languages; Knuth semantics and attributed grammars; query languages and relational models of data. Question answering, inference, and information analysis.

Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.

Syntax and semantics of synthetic (formal and programming) languages; Prolog and logic programming; query languages and relational models of data. Question answering, inference, and information analysis.

Theoretical foundations, algorithms, methodologies, and applications for machine learning. Topics may include supervised methods for regression and classication (linear models, trees, neural networks, ensemble methods, instance-based methods); generative and discriminative probabilistic models; Bayesian parametric learning; density estimation and clustering; Bayesian networks; time series models; dimensionality reduction; programming projects covering a variety of real-world applications.

Syntax and semantics of synthetic (formal and programming) languages; Knuth semantics and attributed grammars; query languages and relational models of data. Prolog, inference from databases and information analysis.