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CSCI 4521 : Introduction to Mobile Robotics

CSCI 4527 : Introduction to Computer Vision

CSCI 4521 : Introduction to Mobile Robotics

CSCI 4525 : Autonomous Robotics: Manipulation

School of Engineering and Applied Sciences

Computer Science (CSCI)

CSCI

3362

Probability for Computer Science

Probablity for Computer Sci

Fall 2017

3

Course Type

Field Work (Internship)

Lecture

Lecture

Default Grading Method

Letter Grade

No

No

CSCI 1311 and Math 1232

Corequisites

30

Frequency of Offering

Every Year

Term(s) Offered

Spring

Are there Course Equivalents?

No

No

Fee Type

No

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.

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.

Uploaded a Course Syllabus

Course Attribute

Key: 2146