CSC 517 Seminar in Statistical Inference, Learning and Models in Data Science

This course will be a reading/seminar course in Statistical Inference, Learning and Models in Data Science that provides an in-depth understanding of the current state of research. Topics to be covered include: Fundamentals of probability theory - the formal language of uncertainty - which is the basis of statistical inference, statistical inference (and its close cousins, data mining and machine learning), parametric and nonparametric inferences, methods for parameter estimation (maximum likelihood estimation, properties of maximum likelihood estimators, consistency of maximum likelihood estimators, Bayesian parameter estimation, minimum variance unbiased estimation), confidence intervals, hypothesis testing, sequential change-point detection, statistical decision theory and a general introduction to statistical models and methods, practical issues such as experimental design and model validations.

Cross Listed Courses

CSC 517 & DA 517