This is a course designed to introduce the key theories at use in sociology and other related academic disciplines, with close attention paid to inequality, solidarity, individualism, bureaucracy and capitalism. The goal is to provide a theoretical web and collaborative learning experiences wherein students will be able to situate social theories and debates in relation to one another, in relation to the theories/perspectives of other disciplines, and also in relation to important issues of the day.

Introduction to sociology. Theory, methods, and approaches to the study of society. The use of several key sociological perspectives such as culture, social class, social psychology, and organizational power to analyze contemporary social issues. (Gen.Ed. SB, DU)

This course covers a broad range of advanced level topics in natural language processing. It is intended for graduate students in computer science who have familiarity with machine learning fundamentals. It may also be appropriate for computationally sophisticated students in linguistics and related areas. Topics include probabilistic models of language, computationally tractable linguistic representations for syntax and semantics, and selected topics in discourse and text mining. After completing the course, students should be able to read and evaluate current NLP research.

Not available at this time

This course will cover several workhorse models for analysis of time series data. The course will begin with a thorough and careful review of linear and general linear regression models, with a focus on model selection and uncertainty quantification. Basic time series concepts will then be introduced. Having built a strong foundation to work from, we will delve into several foundational time series models: autoregressive and vector autoregressive models.

This course is an honors colloquium for COMPSCI 250. Students will explore and discuss topics from the CS250 curriculum in greater detail, with emphasis on logic, and then number theory, recurrences, grammars and automata. Students will do a project presentation on a topic of their choosing.

Distribution and inference for binomial and multinomial variables with contingency tables, generalized linear models, logistic regression for binary responses, logit models for multiple response categories, loglinear models, inference for matched-pairs and correlated clustered data. Prerequisites: Previous course work in probability and mathematical statistics including knowledge of distribution theory, estimation, confidence intervals, hypothesis testing and multiple linear regression; e.g. Stat 516 and Stat 525 (or equivalent). Prior programming experience.

Seminar to inform students of all the different careers/options that are available within the Finance Industry.