Engage with and train in the use of key concepts in machine learning and math: OLS estimator for regression; logistic regression & maximum likelihood estimator; multiple linear regression; principal components …
Explore mathematical foundations of inferential and prediction frameworks, with emphasis on computation, used to learn from data. Frequentist, Bayesian, and Likelihood viewpoints are all considered. Topics: principles of estimation, optimality, …
The data science project course will allow students to take the knowledge gained in each of the four required courses and apply them to a data driven problem. Students will …
Principles of interactivity in application and dashboard development using R, Python, and JavaScript programming languages. Design visually appealing and user-friendly interfaces, develop interactive applications for data visualization, and build dynamic …
Critique models and adapt them to a variety of data sets. Gain a deeper understanding of core ML concepts. Build towards neural networks (latent index models, more complex linear models …
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Principles of interactivity in application and dashboard development using R, Python, and JavaScript programming languages. Design visually appealing and user-friendly interfaces, develop interactive applications for data visualization, and build dynamic …
Explainable artificial intelligence (XAI) is a subfield of machine learning that provides transparency for complex models to connect the technical meaning to social interpretation. Explore interpretability, transparency, and black-box machine …
Understand Deep Learning covering neural networks, activation functions, and optimization algorithms. Gain experience with TensorFlow and PyTorch, mastering key techniques such as Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), …
Explores new models of database design: graph, vector, and ledger. These have become required infrastructure in service of social media (graph databases), Large Language Models (vector databases), and cryptocurrency (ledger …
Comprehensive exploration of the multifaceted aspects of data creation, emphasizing the symbiotic relationship between design and data. Students will gain insight into the intentional and unintentional mechanisms that contribute to …
Introduces complex interplay between technology, regulation, and data science and exposes regulatory realities confronting the field. Read and parse regulatory texts. Navigate the international technology regulatory landscape, identify key actors, …
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This course provides selected special topics in data science.
Study under the direction of a faculty member. Students must obtain approval on a syllabus from a faculty advisor to approve and direct the independent study. Student must be in …
Research under the supervision of faculty member. Student must obtain approval on the description and responsibilities of the research through a syllabus from a faculty advisor. Student must be in …
Covers foundations and applications of NLP with a focus on the most popular form of unstructured data ¿ text. Convert source texts into structure-preserving analytical form and then apply information …
Train your own LLM for a custom task. Learn about the LLM lifecycle from architecture, to pre-training, to supervised finetuning, to deployment, to model editing/updating, including discussing LLM limitations. End …
Provides healthcare domain knowledge, healthcare data understanding, and data science methodologies to solve problems. Understand data types, models, and sources, including electronic health record data; health outcomes, quality, risk, and …
Reinforcement Learning is a dynamic area in machine learning that allows an agent to learn by interacting with its environment. This enables learning when the ground truth is unavailable or …