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
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)
Course evaluations
- Course rating
- 3.39 / 5 · Good
- Instructor rating
- 3.68 / 5 · Very Good
- Terms evaluated
- 10
| Term | Instructor | Course overall | Instructor overall | Well organized | Clear learning objectives | Intellectually challenging | Helpful assignments | Applicable beyond course | Attendance necessary | Weekly effort | Responses |
|---|---|---|---|---|---|---|---|---|---|---|---|
| Spring 2026 | Carl Mctague | 3.3 | 3.7 | 3.7 | 4.3 | 5.0 | 4.3 | 4.0 | 4.3 | 4–6 hrs/week | 3 of 5 |
| Spring 2026 | Usman Khan | 4.3 | 4.6 | 4.6 | 4.5 | 4.8 | 4.3 | 4.6 | 4.4 | 4–6 hrs/week | 28 of 36 |
| Fall 2025 | Carl Mctague | 3.6 | 3.6 | 3.4 | 4.0 | 4.8 | 3.9 | 4.1 | 4.3 | 4–6 hrs/week | 16 of 23 |
| Fall 2025 | Usman Khan | 4.5 | 4.4 | 4.4 | 4.5 | 4.5 | 4.4 | 4.5 | 4.7 | 4–6 hrs/week | 10 of 13 |
| Spring 2025 | Jessica Finocchiaro | 3.3 | 4.0 | 3.9 | 4.0 | 4.7 | 3.7 | 4.1 | 4.1 | 4–6 hrs/week | 39 of 42 |
| Spring 2025 | Sergio Alvarez | 2.8 | 3.2 | 3.9 | 3.8 | 4.6 | 3.8 | 3.5 | 4.5 | 4–6 hrs/week | 12 of 12 |
| Fall 2024 | George Mohler | 4.4 | 4.5 | 4.5 | 4.5 | 4.6 | 4.7 | 4.4 | 4.7 | 1–3 hrs/week | 36 of 41 |
| Fall 2024 | Hsin Hao Su | 4.1 | 4.0 | 4.6 | 4.4 | 4.7 | 4.9 | 4.9 | 5.0 | 4–6 hrs/week | 7 of 11 |
| Spring 2024 | Howard Straubing | 3.7 | 4.1 | 4.3 | 4.4 | 4.5 | 4.3 | 4.3 | 4.4 | 4–6 hrs/week | 78 of 97 |
| Fall 2023 | Carl Mctague | — | — | — | — | — | — | — | — | — | 27 of 42 |
| Spring 2023 | Howard Straubing | 3.1 | 3.4 | 3.6 | 3.8 | 4.5 | 3.8 | 3.6 | 3.6 | 4–6 hrs/week | 51 of 63 |
| Spring 2023 | Sergio Alvarez | 3.1 | 3.4 | 4.1 | 4.3 | 4.8 | 4.2 | 4.0 | 4.3 | — | 28 of 32 |
| Fall 2022 | Jose Bento Ayres Pereira | 1.7 | 2.3 | 2.3 | 2.5 | 4.8 | 3.2 | 3.3 | 4.2 | 10+ hrs/week | 23 of 30 |
| Spring 2022 | Carl Mctague | 2.9 | 3.0 | 3.4 | 3.6 | 4.7 | 3.5 | 3.8 | 2.5 | 7–9 hrs/week | 55 of 97 |
| Fall 2021 | Howard Straubing | 3.6 | 3.7 | 4.1 | 4.1 | 4.6 | 4.4 | 4.1 | 4.1 | 4–6 hrs/week | 24 of 33 |
| Fall 2021 | Hsin Hao Su | 3.1 | 3.2 | 4.0 | 4.0 | 4.6 | 4.1 | 3.7 | 3.7 | 4–6 hrs/week | 10 of 25 |
Data freshness
Instructors
Sections
- Section 01
- Section 01