[Plsugs] FW: Please circulate to your students!

[Tanya Quist]

NEW COURSES:

 

Listed below are the new courses from the School of Information: Science,
Technology & Arts for the Spring 2011 semester. Please distribute to
students who might be interested in generating, managing, and extracting
meaning from the information streaming at us 24/7. The courses will be open
to everyone who has the appropriate prerequisites.

 

Please note: the lab is not yet showing up for ISTA 116, but we are working
on it.

 

ISTA 100, Intro to Information in Science, Technology & Arts:

Important ideas and applications of information science and technology in
the sciences, humanities and arts. Information, entropy, coding; grammar and
parsing; syntax and semantics; networks and relational representations;
decision theory, game theory; and other great ideas from the intellectual
motifs of the Information Age and are explored through applications such as
robotic soccer, chess-playing programs, web search, population genetics
among others.

 

ISTA 116, Statistical Foundations for the Information Age:

Understanding uncertainty and variation in modern data: data summarization
and description, rules of counting and basic probability, data
visualization, graphical data summaries, working with large data sets,
prediction of stochastic outputs from quantitative inputs.  

Operations with statistical computer packages such as R.

 

ISTA 130, Computational Thinking and Doing:

An introduction to computational techniques and using a modern programming
language to solve current problems drawn from science, technology, and the
arts. Topics include control structures, elementary data structures, and
effective program design and implementation techniques. Weekly laboratory.

 

ISTA 410, 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. 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.

 

ISTA 450, Artificial Intelligence:

The methods and tools of Artificial Intelligence used to provide systems
with the ability to autonomously problem solve and reason with uncertain
information.  Topics include: problem solving (search spaces, uninformed and
informed search, games, constraint satisfaction), principles of knowledge
representation and reasoning (propositional and first-order logic, logical
inference, planning), and representing and reasoning with uncertainty
(Bayesian networks, probabilistic inference, decision theory).

 

ISTA 454, Informatics in Biology:

Analyze genomic sequences through understanding and using a variety of
bioinformatics algorithms and software tools.  Interdisciplinary approach
integrating informatics, statistics, and biology.

 

 

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