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Other Courses referencing this course

CSCI 6521 : Introduction to Mobile Robotics

CSCI 6525 : Autonomous Robotics: Manipulation

CSCI 6527 : Introduction to Computer Vision

SEAS 6401 : Data Analytics Capstone I

CSCI 6365 : Advanced Machine Learning

CSCI 6521 : Introduction to Mobile Robotics

CSCI 6525 : Autonomous Robotics: Manipulation

CSCI 6527 : Introduction to Computer Vision

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.

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

Key: 2091