CSCI 2244 Randomness and Computation
This course presents the mathematical and computational tools needed to solve problems that involve randomness. For example, an understanding of random variables allows us to efficiently generate the enormous prime numbers needed for information security, and to quantify the expected performance of a machine learning algorithm beyond a small data sample. An understanding of covariance allows high quality compression of audio and video. Topics include combinatorics and counting, random experiments and probability, random variables and distributions, computational modeling of randomness, Bayes' rule, laws of large numbers, vectors and matrices, covariance and principal axes, and Markov chains.
Course overview
- Department
- Computer Science
- School
- MCAS
- Credits
- 3
- Level
- Undergraduate
- Offered
- Every Fall,Every Spring
Catalog details
- Prerequisites
- CSCI1101 and CSCI2243 or MATH2216 and MATH1103
Requirements fulfilled
- Computer Science B.A.: CSCI 2000-level-or-higher elective (Current University Catalog; students should confirm their catalog year)
- Mathematics B.S.: Natural science, computer science, or economics corequisites (Current University Catalog; students should confirm their catalog year)
- Computer Science B.A.: Required computer science core (Current University Catalog; students should confirm their catalog year)
- Computer Science B.S.: Required computer science core (Current University Catalog; students should confirm their catalog year)
Official evaluation summary
3.39 / 5
Data freshness
Instructors
- Usman Khan
- Carl Mctague
- George Mohler
- Howard Straubing
- Hsin Hao Su
- Jessica Finocchiaro
- Jose Bento Ayres Pereira
- Sergio Alvarez
Sections
- Section 01