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DEGREE REGULATIONS & PROGRAMMES OF STUDY 2024/2025

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DRPS : Course Catalogue : School of Informatics : Informatics

Undergraduate Course: Computer Vision (UG) (INFR11278)

Course Outline
SchoolSchool of Informatics CollegeCollege of Science and Engineering
Credit level (Normal year taken)SCQF Level 11 (Year 4 Undergraduate) AvailabilityAvailable to all students
SCQF Credits20 ECTS Credits10
SummaryThis course follows the delivery and assessment of Computer Vision (INFR11212) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11212 instead.
Course description This course follows the delivery and assessment of Computer Vision (INFR11212) exactly. Undergraduate students must register for this course, while MSc students must register for INFR11212 instead.
Entry Requirements (not applicable to Visiting Students)
Pre-requisites Co-requisites
Prohibited Combinations Other requirements None
Information for Visiting Students
Pre-requisites Students should check these maths and programming requirements carefully, as the course assumes and builds on these foundations. Experience has shown that students without this background can struggle with the course.

Maths requirements:
1. Linear algebra: Vectors: scalar (dot) product, transpose, unit vectors, vector length, orthogonality. Matrices: addition, matrix multiplication, matrix inversion. Eigenvectors, determinants quadratic forms
2. Special functions: properties and combination rules for logarithm and exponential
3. Calculus: Rules for differentiation of standard functions. Functions of several variables. Partial differentiation. Multivariate maxima and minima
4. Geometry: Basics of lines, planes and hyperplanes. Coordinate geometry of circle, sphere, ellipse, ellipsoid and n-dimensional generalizations
5. Probability theory: Discrete and continuous univariate random variables. Expectation, variance. Univariate Gaussian distribution. Joint and conditional distributions

Machine Learning requirements:
This course assumes students are familiar with concepts from machine learning such as supervised training, feature selection, loss functions, and optimization. It is strongly recommended that students who register for this course have either taken a machine learning course previously or are registered for one in Semester 1.

Programming requirements:
Students should be familiar with programming in a modern object-oriented language, ideally Python which is the course language.
High Demand Course? Yes
Course Delivery Information
Academic year 2024/25, Available to all students (SV1) Quota:  None
Course Start Semester 2
Timetable Timetable
Learning and Teaching activities (Further Info) Total Hours: 200 ( Lecture Hours 20, Programme Level Learning and Teaching Hours 4, Directed Learning and Independent Learning Hours 176 )
Assessment (Further Info) Written Exam 50 %, Coursework 50 %, Practical Exam 0 %
Additional Information (Assessment) 50% exam
50% coursework
Feedback Written feedback on coursework
Exam Information
Exam Diet Paper Name Hours & Minutes
Main Exam Diet S2 (April/May) Computer Vision (UG) (INFR11278)2:120
Learning Outcomes
On completion of this course, the student will be able to:
  1. Define and explain principles underpinning computer vision methods
  2. Describe current vision problem settings and their current solutions
  3. Implement, train and debug computer vision models
  4. Design, explain, analyse, and compare the behaviour of computer vision models under different settings
  5. Identify social and ethical implications of computer vision methods in the real world
Reading List
Foundations of Computer Vision (Torralba, Isola, Freeman). This will be the main textbook. It is more comprehensive than the scope of the course.
Computer Vision (Rick Szeliski)
Deep Learning (Yoshua Bengio)
Additional Information
Graduate Attributes and Skills Research and enquiry:
- Problem-solving, debugging and understanding the behavior of systems under different scenarios
- Critical / analytical thinking of methods, their advantages and disadvantages
- Describing partial evidence or results, and reasoning under uncertainty

Personal effectiveness:
- Planning and organizing own time, to achieve milestones at particular deadlines

Personal responsibility and autonomy:
- Learning independently, researching how others have addressed the same issue
- Develop creativity to address problems in existing methods

Communication:
- Work is done in pairs, so interpersonal/teamwork skills will be developed
- The coursework involves writing a report, so verbal and written communication will be developed
KeywordsNot entered
Contacts
Course organiserMs Laura Sevilla-Lara
Tel:
Email: lsevilla@ed.ac.uk
Course secretaryMiss Yesica Marco Azorin
Tel: (0131 6)50 5194
Email: ymarcoa@ed.ac.uk
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