Learning Outcomes:
Ability to encode large amounts of information using matrices and then efficiently extracting important parts using linear algebra. Understanding stochastic matrices and how to apply their properties in ranking data.
Curricular information is subject to change.
Ability to encode large amounts of information using matrices and then efficiently extracting important parts using linear algebra. Understanding stochastic matrices and how to apply their properties in ranking data.
Student Effort Type | Hours |
---|---|
Autonomous Student Learning | 70 |
Lectures | 24 |
Online Learning | 12 |
Total | 106 |
A knowledge of basic linear algebra (covered in first level linear algebra modules) is needed.
Description | Timing | Component Scale | % of Final Grade | ||
---|---|---|---|---|---|
Assignment(Including Essay): Homework Assignments, every two weeks. | Week 4, Week 6, Week 8, Week 10, Week 12 | Graded | No | 30 |
No |
Exam (In-person): Final, closed-book exam. | End of trimester Duration: 2 hr(s) |
Standard conversion grade scale 40% | No | 70 |
No |
Resit In | Terminal Exam |
---|---|
Autumn | Yes - 2 Hour |
• Group/class feedback, post-assessment
Not yet recorded.
Name | Role |
---|---|
Ms Ciara Murphy | Tutor |
Spring | Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Thurs 15:00 - 15:50 |
Spring | Lecture | Offering 1 | Week(s) - 20, 21, 22, 23, 24, 25, 26, 29, 30, 31, 32, 33 | Tues 16:00 - 16:50 |