Teaching

BIOINF 2119 Probabilistic Methods in Artificial Intelligence (Onsite course)
Taught every spring, 2010 - 2017

This course introduces fundamental concepts and methods in artificial intelligence that are applicable to problems in biomedicine. This course is designed for students who do not necessarily have a background in computer science. The course provides the foundations in artificial intelligence methods including search (breadth-first search, depth-first search, greedy search, etc), probabilistic knowledge representation and reasoning (Bayesian networks: model, independencies, semantics, parameter estimation and inference), decision theory, and machine learning (regression, neural networks, classification trees, support vector machines, Markov models and hidden Markov models).

BIOINF 2011 Foundations of Clinical and Population Informatics (Online course)
Taught every spring, 2013 - 2014

A key goal in informatics is to represent biomedical data and knowledge in a form that can be readily used by computers, and to apply computer-based methods to biomedical data to enable computers to be useful in clinical care, in public health and in biomedical research. To address these goals, this online course introduces and provides an overview of the foundational concepts in health informatics. The course covers a variety of topics such as biomedical data and electronic health records, ontologies and standards used in biomedicine, computerized clinical decision support systems, evaluation of clinical computer systems, and computational methods that underlie clinical computer systems such as symbolic reasoning and probabilistic reasoning.

BIOINF 2011: Foundations of Clinical and Population Informatics (Onsite course)
Taught every fall, 2007 - 2011

This onsite course introduces and provides an overview of the foundational concepts of clinical and public health informatics. The course covers a variety of topics such as biomedical data and electronic health records, ontologies and standards used in biomedicine, computerized clinical decision support systems, evaluation of clinical computer systems, and computational methods that underlie clinical computer systems such as symbolic reasoning and probabilistic reasoning.