KV6021 - Machine Learning

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 implement program code and analyse/solve machine learning problems in Northumbria’s CIS building computer labs, which are fully equipped with the latest industry-standard software. Some 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 intended to aid your understanding.

All module material will be available on the eLearning Portal (ELP) so that you can access to all lecture and seminar materials when you need to.

Your tutors will provide you with feedback on your work in the practical sessions to help you and then assess your progress. Additionally, the university library offers support for all students through its catalogue and an Ask4Help Online service.

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)

Knowledge & Understanding:
ML01 - Demonstrate a comprehensive and detailed knowledge and critical understanding of machine learning systems
ML02 - Demonstrate a detailed understanding of theory and an awareness of computational and practical challenges of machine learning systems

Intellectual / Professional skills & abilities:
ML03 - Analysing machine learning systems and their practical use in real life applications (Natural Language Processing, Medical Diagnosis, Speech Recognition)
ML04 - Apply and critically evaluate techniques to solve problems in machine learning systems

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
ML05 – Critically analyse legal, ethical and social issues in machine learning systems as well as current technologies for cyber security

The summative assessment (100%) for the module is a single individual assignment that combines programming/implementation of machine learning models and discursive writing. For this, you will investigate real-life machine learning problems (e.g. for Natural Language Processing, Medical Diagnosis, Speech Recognition), and will develop and critically evaluate models 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. The word limit will be 1500).
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 practical workshops and 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.

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This module introduces theoretical foundations and practical examples of advance level of machine learning techniques/concepts to you. 'Machine learning' module will provide you with a thorough understanding of both traditional and state-of-the-art methodologies, awareness of important implementation aspects e.g. scalability, tuning the hyper-parameters, choosing the right model according to the data/application domain. In your assessment, you will implement these methods and fine tune your model parameters or hyper-parameters to solve a specific practical problem in relevant research areas e.g. Natural Language Processing, Medical Diagnosis, Speech Recognition, and you will be provided feedback on your practical work.

This research led module enables you to gain a broad range of skills including analytical, problem solving, creativity, conducting research by addressing legal, ethical, social issues properly, and in return improves your employability.