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Introduction to Data-Intensive Empirical Research

This 7 session course provides the basics of empirical research for undergraduate or graduate students in any field of studies. The students will learn about the scientific process, scientific writing, and will also have a practical capstone which they can use to start their own research projects. Each module features a theoretical background and a hands-on activity. When participating in the activities, students have the opportunity to reflect on their research interests and receive feedback from the instructor and their peers. 

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Introduction to Bayesian Modeling

Learn key concepts and practices of Bayesian modeling. Explore models with conjugate priors, illustrating with the Beta-Binomial and Gamma-Poisson models, Markov Chain Monte Carlo (MCMC), variational inference (VI) methods for Bayesian inference, and semi-conjugate models, including Bayesian linear regression, Bayesian mixture models, and Bayesian hidden Markov models. Also, dig deeper into models used when there are not conjugate priors, such as Bayesian logistic regression, Bayesian multiclass regression, a racially polarized voting model, and Bayesian deep learning.

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Matrix Methods for Machine Learning

Basic linear algebra operations and their use in approximations underlying machine learning, with applications to solving regression, classification, and clustering problems.

Students completing the course will be able to utilize linear algebra to represent system behavior, fit a linear algebra model to real-world data and their use in deriving optima, and utilize linear algebra for dimensionality reduction and to evaluate the quality of a regression, classification, or clustering model.

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