CSCI2244 · Computer Science
Morrissey College of Arts & Sciences
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 experience
Averages use the original five-point historical evaluation scale.
Organization
4.0 / 5
How well the course was organized
Challenge
4.7 / 5
How intellectually challenging students found it
Attendance
4.1 / 5
How necessary attendance was
Assignments
4.0 / 5
How helpful assignments were
Weekly effort
~5.5
hours per week
Estimated from the original workload response buckets. Individual sections may differ.
Instructor options
Ratings below reflect only recovered evaluations connected to this course.
Assistant Professor of the Practice, Computer Science
3.7 / 5
3.5 / 5
Professor, Computer Science
3.9 / 5
3.5 / 5
Associate Professor, Computer Science
3.5 / 5
3.1 / 5
Associate Professor, Computer Science
3.6 / 5
3.6 / 5
Associate Professor, Computer Science
2.6 / 5
2.3 / 5
Professor, Computer Science
4.5 / 5
4.4 / 5
Assistant Professor, Computer Science
4.0 / 5
3.3 / 5
Across time
Section-level results available in the recovered archive.
Spring 2025
2 sectionsFall 2024
2 sectionsSpring 2024
2 sectionsFall 2023
1 sectionSpring 2023
2 sectionsFall 2022
2 sectionsSpring 2022
2 sectionsFall 2021
2 sectionsSpring 2021
2 sectionsFall 2020
1 section