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Viewing: CSCI 3362 : Probability for Computer Science

Last approved: Mon, 20 Feb 2017 09:03:54 GMT

Last edit: Thu, 16 Feb 2017 15:45:54 GMT

Catalog Pages referencing this course
Programs referencing this course
School of Engineering and Applied Sciences
Computer Science (CSCI)
Probability for Computer Science
Probablity for Computer Sci
Fall 2017
Course Type
Field Work (Internship)
Default Grading Method
Letter Grade

CSCI 1311 and Math 1232


Frequency of Offering
Every Year
Term(s) Offered
Are there Course Equivalents?
Fee Type


Introduction to probability and statistics for computer scientists; random variables; conditional probability, independence, correlation; applications to computer science, including 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 students. 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.
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Course Attribute

riffat (Wed, 15 Feb 2017 15:27:48 GMT): Rollback: Course description in Word doc has to match with Courseleaf description.
Key: 2146