Computational Science (MS)

In this course you will study computer algorithms that automatically acquire new knowledge and improve their own performance through experience. You will cover theory and implementation of algorithms for machine learning. Topics include linear and logistic regression, Bayesian networks and learning, decision trees, kernel/support-vector machines, unsupervised learning, reinforcement learning, and other emerging algorithms. Short programming assignments and/or projects with hands-on experiments with various learning algorithms will be given.

This course provides you with the study of computational systems that understand and generate human language. You will cover fundamental concepts and techniques, such as lexical and ontological semantics, word sense disambiguation, syntactic and semantic parsing, summarization, and generation. Additional emphasis will be on the different traditions and theoretical frameworks that informed the theories and algorithms used for these solutions, namely, linguistics, statistics and computer science, and the history of their struggle from the 1950s until today.

The objective of this course is to provide you with the knowledge and understanding of the methods of statistical analysis applicable to biological research. Emphasis will be placed on the concepts and application of statistical thinking. Basic probability theory, parametric and non-parametric statistics including t-test, analysis of variance, correlation and simple linear regression will be reviewed. Advanced statistical methods including multiple regression, logistic regression, model selection and other quantitative methods will be introduced.