What will I learn on this module?
This module will give you a foundational knowledge of machine learning (ML) and artificial intelligence (AI) concepts and related Microsoft Azure services. This module will help you demonstrate knowledge of common ML and AI workloads. In addition, ‘AI Fundamentals’ will guide you through hands-on sessions and workshops on how to implement common ML and AI workloads on MS Azure.
‘AI Fundamentals’ will prepare you for later modules, as well as for a placement in your third year. Employers look for skilled graduates with industry-recognised and technology-based certifications such as the MS Azure AI Fundamentals AI-900. Employers are seeking talented individuals who can work as members of a team in understanding, analysing, and designing AI solutions leading to sustainable growth, change and impact and applying effective, responsible and ethical AI-enabled techniques.
During ‘AI Fundamentals’, students will work through a series of workshops and exercises relying on state-of-the-art AI technologies and Cloud Computing such as MS Azure. Furthermore, students will make use of Northumbria’s state-of-the-art computer labs. Students will also critically engage with research outputs as part of their research-rich learning.
‘AI fundamentals’ lectures and workshops will help students prepare for the AI Fundamentals certification exam (AI-900). This certification can be used to prepare for other Azure role-based certifications like Azure Data Scientist Associate or Azure AI Engineer Associate which are much sought-after by recruiters.
How will I learn on this module?
You will learn through lectures, workshops, and independent learning. The lectures will cover theories and concepts that will enable you to tackle a series of guided exercises. You will work on these during workshops and hands-on sessions in Northumbria’s CIS building computer labs, which are fully equipped with the latest industry-standard software.
How will I be supported academically on this module?
You will be supported by lecturers during the timetabled sessions when you will receive feedback on your work. The University’s eLearning Portal offers remote access to all lecture and seminar materials to reinforce your learning. In addition, the university library offers support for all students through providing electronic resources
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 knowledge, and basic understanding, of essential facts, concepts, principles, theories, techniques, and technologies related to computing, computer science, data science and Artificial Intelligence (AI) workloads.
ML02 - Describe AI workloads and considerations, fundamental principles, features of AI workloads and their implementation on Azure.
ML03 - Specify, design and construct simple computer, AI and data-based systems.
Intellectual / Professional skills & abilities:
ML04 - Specify, design and construct simple computer- and AI- based systems (IPSA).
Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
ML05 – Demonstrate a basic awareness of the global, ethical, and cultural issues related to computing, and specifically AI and data - and its societal implications for equality and diversity.
How will I be assessed?
Individual essay (100% of the final mark)
Summative assessment will be a single assignment involving a mixture of AI written tasks (totalling no more than 2000 words), AI model designs and building no code AI-based services. The assessment will be submitted electronically.
This summative assessment addresses Module Learning Outcomes MLO1, 2, 3, 4, 5
Formative assessment will take the form of exercises in workshop classes to help you apply the concepts covered in the lectures and to check your understanding. Your tutors will provide you with feedback on your work. Computer-based multiple-choice tests will help check your basic knowledge outside of class time.
Pre-requisite(s)
N/A
Co-requisite(s)
N/A
Module abstract
This module aims to help students identify features of common AI workloads, understand principles for responsible AI, identify common machine learning types, describe core machine learning concepts, core tasks in creating a machine learning solution, identify common types of computer vision solution, Azure tools and services for computer vision task, features of common NLP Workload Scenarios, Azure tools and services for NLP workloads, identify common use cases for conversational AI, describe capabilities of no-code machine learning with Azure Machine Learning studio, and identify Azure services for conversational AI. Theory is followed by workshops where you will be introduced to different Azure services for example
Students will be asked to produce a critical reflection on an AI topic: 2000 words essay (50% of the final mark) which will require substantial student-centred enquiry and research.
Students will also be asked to present the implementation of their proposed solution and discuss the findings (50% of the final mark)
Course info
UCAS Code G415
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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