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.
The module is designed to provide an introduction to applied linear algebra with an emphasis on topics used in large data analysis, and in particualar in Google search engine.
The Google search engine has made accessing information easy and its speed and efficacy amazes people. This module explains how it works. An essential component is the ability to rank information according to its importance, and in Google, this relies on simple algebraic principles. The mathematical areas involved are linear algebra and matrix analysis.
The presentation will in part be based on the book by Amy Langville and Carl Meyer "Google's PageRank and Beyond: The Science of Search Engine Rankings" (Princeton University Press 2006)
The Google search engine has made accessing information easy and its speed and efficacy amazes people. This module explains how it works. An essential component is the ability to rank information according to its importance, and in Google, this relies on simple algebraic principles. The mathematical areas involved are linear algebra and matrix analysis.
The presentation will in part be based on the book by Amy Langville and Carl Meyer "Google's PageRank and Beyond: The Science of Search Engine Rankings" (Princeton University Press 2006)
About this Module
Student Effort Hours:
| Student Effort Type | Hours |
|---|---|
| Lectures | 24 |
| Autonomous Student Learning | 70 |
| Online Learning | 12 |
| Total | 106 |
Approaches to Teaching and Learning:
Lectures,Flipped classroom (some lecture hours might be transformed in Q&A sessions)
Online Tutorials,
Enquiry and Problem-Based Learning.
Requirements, Exclusions and Recommendations
Learning Requirements:A knowledge of basic linear algebra (covered in first level linear algebra modules) is needed.
Module Requisites and Incompatibles
Not applicable to this module.Assessment Strategy
| 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 |
Feedback Strategy/Strategies
• Group/class feedback, post-assessment
How will my Feedback be Delivered?
Not yet recorded.
| Name | Role |
|---|---|
| Ms Ciara Murphy | Tutor |
Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
| 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 |