This course is designed for capstone project teams to meet in groups, with advisors, and with clients to advance work on their projects.
Designed for capstone project teams to meet in groups with advisors and clients to advance work on their projects. Capstone course is for MSDS students.
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 …
This course covers fundamentals of data mining and machine learning within a common statistical framework. Topics include regression, classification, clustering, resampling, regularization, tree-based methods, ensembles, boosting, and Support Vector Machines. …
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 …
Understand machine learning fundamentals, cybersecurity, and deep learning principles. Explore how machine learning algorithms can be leveraged to address prevalent cybersecurity issues, such as malware detection, spam filtering, anomaly detection, …
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, …
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 …
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 …
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 …
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 …
Covers the fundamentals of probability and stochastic processes. Students will become conversant in the tools of probability, clearly describing and implementing concepts related to random variables, properties of probability, distributions, …
Explores the mathematical foundations of inferential and prediction frameworks commonly used to learn from data. Frequentist, Bayesian, Likelihood viewpoints are considered. Topics include: principles of estimation, optimality, bias, variance, consistency, …
Introduction to regression modeling. Topics will be discussed first in the context of linear regression, and then revisited in the context of logistic regression, ordinal regression, proportional hazards regression, and …
Introduces physics-aware deep learning (PADL), an emerging approach that embeds physical laws into neural networks for accurate, efficient modeling. Topics include differential equations, physics-informed neural networks, neural operators, and PyTorch …
Fundamentals of data mining and machine learning within a common statistical framework. Topics include boosting, ensembles, Support Vector Machines, model-based clustering, forecasting, neural networks, recommender systems, market basket analysis, and …
Provides an exploration of foundational concepts in modern time series modeling and analysis. The course covers both classical statistical and signal processing methods and contemporary deep learning models.
Course content varies by section and is selected to fill timely, special interests and needs of students. May be repeated for credit when topic varies.
This course provides the opportunity to offer a new topic in the subject area of data science.
Covers data pipeline: techniques to collect data, organize, query & apply the data, and generate products that describe the insights. Topics include Python environments, containers using Docker, data wrangling with …