Comprehensive introduction to predictive modeling, a cornerstone of data science and machine learning. Learn the fundamental concepts, techniques, and tools used to build models while emphasizing both theoretical understanding and practical applications. The topics include we will cover are an in-depth analysis of linear models and different variants, their extension to generalized linear models, and an introduction to nonparametric regression.

This course introduces first-year graduate students in the humanities and social sciences to the knowledge and skills fundamental to success in graduate school. Particular topics vary.

Evolution of language models, from encoding words to simple vectors to training LLMs. Train and build LLM, understand concepts like self- and cross-attention in LLMs and their applications, review research on Tokenizers, Retrieval Augmented Generation (RAG), Prompt Engineering, Fine-tuning LLMs using Low-Rank Adapters (LoRA), Quantization in LLMs, QLoRA, In-context Learning (ICL) and Chain-of-Thought (CoT) reasoning. Using Python libraries.

This course is designed for first year Graduate students in the Computer Engineering Program to help orient new graduate students to the current research topics, available research tools, software and systems, publishing systems, and other topics to help new students become successful.Prerequisite: CpE grduate student or instructor permission

Covers the fundamental concepts of uncertainty in artificial intelligence (AI). Students will explore various techniques and models used to handle uncertainty in AI and machine learning systems, including Bayesian deep learning, dropout as a Bayesian approximation, and decision theory. Will also cover applications of uncertainty in AI, such as computer vision, natural language processing,and autonomous systems.

Project oriented course that will research specific climate problems, proposing new solution to decision makers at local & state level. Course expands understanding of broad societal scope relevant to climate action. Students are exposed to federal, state & local policy challenges and opportunities as well as understanding how business & politics shape the policy landscape. Gain understanding of diverse climate-relevant career opportunities.

Course uses expert entrepreneurs with decades of starting & running new ventures. Expert entrepreneurs learn to tackle the unpredictable, but also to embrace and leverage it to cocreate enduring new ventures. Students will grapple with the principles and process of effectual action and interaction. The course is designed to delve into effectual entrepreneurship ¿ philosophically, psychologically and practically.

Course explores the integration of moral & ethical considerations in addressing U.S. public policy challenges. Students study & contrast major philosophical & political theories of justice & the common good, including those that are embedded in the U.S. constitutional architecture; and consider and contrast how these theories would guide public policy choices.

Introduces classic and contemporary theory and research on the social psychology of stigma, primarily from the perspective of the stigmatized. Topics include stigma's origin and nature, stigma and self-concept, stereotype threat, attributional ambiguity, stigma and social interaction, and implications of stigma for education, health, and life attainment more generally. Provides an overview of this area of psychology and its policy implications. Prerequisites: Graduate Student

Consider the effect of public opinion on policy in the U.S. What do policymakers believe about citizens' preferences? Whose opinion matters, when does it matter, & why? Do policies always reflect the majority? How has partisan polarization affected policy-making in recent years? Examine questions through lens of some of today's most pressing issues, including immigration policy, social welfare programs, military spending, abortion, & more.