What will I learn on this module?
This module will provide you with knowledge and understanding of traditional, as well as modern and advanced machine learning techniques, and theoretical foundations of the algorithms, and enable you to gain practical experiences for applying these techniques to solve problems in areas such as Natural Language Processing, Image Processing, Medical Diagnosis, Speech Recognition.
‘Machine Learning’ aims to improve your employability by enabling you to gain a broad range of skills including analytical, problem solving, creativity, conducting research by addressing legal, ethical, social issues properly.
In particular, you will cover topics such as:
• Supervised machine learning techniques and classifiers
• Unsupervised machine learning techniques, clustering
• Feature extraction (supervised and unsupervised)
• Ensemble models
• Modern and advanced models
• Application of machine learning techniques in e.g. Natural Language Processing (NLP), Image Processing, Medical Diagnosis
• Legal, ethical and social issues in your applications, and techniques for security.
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 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.
How will I be supported academically on this module?
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.
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:
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
How will I be assessed?
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.
Pre-requisite(s)
N/A
Co-requisite(s)
N/A
Module abstract
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.
Course info
UCAS Code G407
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
Start September 2024 or September 2025
All information is accurate at the time of sharing.
Full time Courses are primarily delivered via on-campus face to face learning but could include elements of online learning. Most courses run as planned and as promoted on our website and via our marketing materials, but if there are any substantial changes (as determined by the Competition and Markets Authority) to a course or there is the potential that course may be withdrawn, we will notify all affected applicants as soon as possible with advice and guidance regarding their options. It is also important to be aware that optional modules listed on course pages may be subject to change depending on uptake numbers each year.
Contact time is subject to increase or decrease in line with possible restrictions imposed by the government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors if this is deemed necessary in future.
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