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Viewing: CSCI 6362 : Probability for Computer Science

Last approved: Wed, 05 Apr 2017 08:01:57 GMT

Last edit: Tue, 21 Feb 2017 23:16:47 GMT

Programs referencing this course
School of Engineering and Applied Sciences
Computer Science (CSCI)
CSCI
6362
Probability for Computer Science
Probality for Computer Sci
Spring 2017
3
Course Type
Lecture
Default Grading Method
Letter Grade

No
No

Corequisites

36

Frequency of Offering
Every Year
Term(s) Offered
Spring
Are there Course Equivalents?
No
 
No
Fee Type


No


Concepts of probability and statistics used in computer science; random variables; conditional probability, independence, correlation; law of large numbers, central limit theorem; applications to computer science, including entropy, information theory, data compression, coding, inference, Markov chains, randomized algorithms. Students cannot receive credit for both CSCI 3362 taken while an undergraduate and CSCI 6362 taken while a graduate student. Students in the combined BS/MS program cannot receive credit for both CSCI 3362 and CSCI 6362.
1. Reason under uncertainty, including probabilistic reasoning, and statistical inference.
2. Formulate real-­‐world problems in probabilistic models, and then analyze them.
3. Conceptualize core topics in probability and statistics that are useful in many areas of computer science.
4. Understand randomized analyses of algorithms.
5. Apply knowledge of mathematics, science and engineering.
6. Analyze and interpret data from a probabilistic viewpoint.

Course Attribute


Key: 2091