Data Science

INFO 521: Introduction to Machine Learning

Machine learning describes the development of algorithms, which can modify their internal parameters (i.e., "learn") to recognize patterns and make decisions based on example data. These examples can be provided by a human, or they can be gathered automatically as part of the learning algorithm itself.

INFO 515: Organization of Information

Introduction to the theories and practices used in the organization of information. Overview of national and international standards and practices for access to information in collections.

INFO 514: Computational Social Science

This course will guide students through advanced applications of computational methods for social science research.  Students will be encouraged to consider social problems from across sectors, including health science, environmental policy, education, and business.

INFO 510: Bayesian Modeling and Inference

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.

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