Bayesian inferential methods provide a foundation for machine learning under conditions of uncertainty. Bayesian machine learning techniques can help us to more effectively address the limits to our understanding of world problems. This class covers the major related techniques, including Bayesian inference, conjugate prior probabilities, naive Bayes classifiers, expectation maximization, Markov chain monte carlo, and variational inference. A course covering statistical techniques such as regression.

Students will develop a detailed understanding of the legal aspects of public employment law, and the short and long-term impact of recruiting and retaining talented employees. Emphasis will be placed on the means by which evidence-based strategies may be applied to determine the appropriate number of resources to deploy to normal and complex operations. Prereq: Admission to MPS Degree Program

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.

A graduate-level course on deep learning fundamentals and applications with emphasis on their broad applicability to problems across a range of disciplines. Topics include regularization, optimization, convolutional networks, sequence modeling, generative learning, instance-based learning, and deep reinforcement learning. Students will complete several substantive programming assignments. A course covering statistical techniques such as regression.

Course provides an introduction to leadership in the public arena. Through course readings, team projects, and discussion of case studies, students will develop skill at identifying the resources, options, and constraints of leaders and followers in different organizational and political settings, writing policy memos, making professional policy presentations, developing negotiation strategies, managing uncertainty and stress, & working in teams.

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.

An introductory course in which principles of assessing educational policies are applied to the evidence currently available across a range of policies. Areas of education policy may include early childhood education, charter schools, accountability, teacher recruitment, retention and assessment, and bridging from K-12 to high education. Discussions focus on linking policies to outcomes for students.

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

Introduces fundamental concepts of computation, data structures, algorithms, & databases, focusing on their role in data science. Covers both theoretical studies & hands-on learning activities. Includes basic data structures, advanced data structures, searching, sorting, greedy algorithms, linear programming, & basics of databases. Will develop computational thinking skills and learn a variety of ways to represent & analyze real-world data.

Many problems in data science essentially boil down to some mathematical relationships that are to be solved numerically. But have you ever wondered how computers could do math? This graduate-level data science course aims to cover fundamental topics of scientific computing, specifically selected and curated for data scientists, including numerical errors, root finding algorithms, numerical linear algebra, and numerical optimization.