Bayesian modeling and inference is a powerful modern approach to representing the statistics of the world, reasoning about the world in the face of uncertainty, and learning about it from data. It cleanly separates the notions of representation, reasoning, and learning. It provides a principled framework for combining multiple source of information such as prior knowledge about the world with evidence about a particular case in observed data. This course will provide a solid introduction to the methodology and associated techniques, and show how they are applied in diverse domains ranging from computer vision to molecular biology to astronomy. Graduate-level requirements include different exams requiring greater depth of understanding of topics, and will be assigned questions based on graduate-student specific assignments topics.
As we work together to battle the coronavirus, we will continue to offer safe and secure online sessions . Even though our physical office is closed, in accordance with the guidelines recommended by CDC, we are working remotely and continuing to provide student, staff, and faculty assistance. We can be reached Monday-Friday 9am-4pm Mountain Standard Time at 520-621-3565 or by email – please refer to the iSchool Directory. Please allow up to 24 hours response time. Faculty and Adjuncts will respond as their schedules permit.
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