KF6052 - Machine Learning and Computer Vision

What will I learn on this module?

The aim of the module is to provide you with knowledge and understanding of machine learning techniques and computer vision systems, including how to solve problems in these areas. In particular, you will cover topics such as:

• Machine learning
• Supervised machine learning techniques and classifiers
• Unsupervised machine learning techniques
• Computer vision and digital image fundamentals
• Legal, ethical and social issues in computer vision, and techniques for security.
• Application of machine learning techniques in computer vision (biometric systems, face, iris, fingerprint, etc.)

How will I learn on this module?

You will learn about concepts, mathematical principles and theories in formal lectures and these will be demonstrated, practised and discussed in practical workshop sessions. During these practical sessions you will complete/write program code and analyse/solve machine learning and computer vision problems. Two guided independent learning homework exercises will also be set for you to work on outside of class time. These will include problems in machine learning and computer vision intended to aid your understanding.

All module material will be available on the eLearning Portal (ELP) so that you can access information when you need to. The university library offers support for all students through its catalogue and an Ask4Help Online service.

How will I be supported academically on this module?

Your tutors will provide you with feedback on your work in the practical sessions and on the homework exercises to help you and them assess your progress.

What will I be expected to read on this module?

All modules at Northumbria include a range of reading materials that students are expected to engage with. The reading list for this module can be found at: http://readinglists.northumbria.ac.uk
(Reading List service online guide for academic staff this containing contact details for the Reading List team – http://library.northumbria.ac.uk/readinglists)

What will I be expected to achieve?

Knowledge & Understanding:

1. Demonstrate a comprehensive and detailed knowledge and critical understanding of the theory and practice of modern digital techniques in computer vision
2. Demonstrate a comprehensive understanding of machine learning systems and their applications.

Intellectual / Professional skills & abilities:
3. Apply and critically evaluate techniques to solve problems in machine learning and computer vision systems.

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
4. Critically analyse legal, ethical and social issues in computer vision as well as current technologies for cyber security

How will I be assessed?

Summative assessment

The summative assessment for the module
is a single individual assignment that combines programming and discursive writing. For this you will investigate computer vision and machine learning problems, and will develop and critically evaluate systems to solve them. The assessment will rely heavily on the work done during the practical lab sessions and will assess all of the module’s MLOs.

Written feedback on the summative assignment will be provided to you in the form of detailed marks and comments, highlighting strengths and improvements.

Formative assessment and feedback

The exercises in the practical workshops and two homework exercises are intended to help you and your tutor formatively assess your understanding and progress. You will be provided with feedback on these by your tutor.





Module abstract

The aim of the module is to provide you with knowledge and understanding of machine learning techniques and computer vision systems, including how to solving problems in these areas.

Course info

UCAS Code G405

Credits 20

Level of Study Undergraduate

Mode of Study 3 years full-time or 4 years with a placement (sandwich)/study abroad

Department Computer and Information Sciences

Location City Campus, Northumbria University

City Newcastle

Fee Information

Module Information

All information is accurate at the time of sharing.

Full time Courses starting in 2023 are primarily delivered via on-campus face to face learning but may include elements of online learning. We continue to monitor government and local authority guidance in relation to Covid-19 and we are ready and able to flex accordingly to ensure the health and safety of our students and staff.

Contact time is subject to increase or decrease in line with additional restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors, potentially to a full online offer, should further restrictions be deemed necessary in future. Our online activity will be delivered through Blackboard Ultra, enabling collaboration, connection and engagement with materials and people.


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