Teaching

The course will enable students to understand and evaluate major philosophical and technological discussions surrounding AI and its applications.

The course introduces students to basic methods in image processing and image understanding, with a number of applications.

An introduction to retrieval from image, video, and audio databases, from a statistical, pattern recognition, and algorithmic point of view.

The course covers methods for computational analysis of digital data and physical evidence. An emphasis is on the use of statistical, pattern recognition, and machine learning techniques. We will be examining how personal information and identity can be leaked and what techniques there are for protecting personal information.

Simulation and self-organization covers computational topics related to economics, ecology, and evolution. Techniques covered include differential equations, stochastic methods, and multi-agent simulations.

Natural language processing is a key technology in web search, information retrieval, social network analysis, machine translation, speech recognition, and many other applications. The course introduces students to methods for natural language processing, natural language understanding, and information retrieval.

A course covering the basics of pattern recognition and image understanding aimed at upper level undergraduate students.

A course about the intersection of computer science and biology: information processing in neurons, self-assembly and self organization, brain function, and neural networks.

Most of the data we interact with day-to-day does not come in the form of data structures or databases, but instead in the form of documents and document images. This course introduces students to the formats, techniques, and algorithms used for representing, compressing, analyzing, processing, and displaying documents. Topics covered include:

An introduction to pattern recognition and statistical classifiers.