PE7047 - AI Studio

What will I learn on this module?

The aim of the module is to provide you with knowledge and understanding of artificial intelligence techniques and digital signal and image processing systems, including how to solving problems in these areas. In particular, you will cover topics such as:

• Introduction to artificial intelligence
• Supervised machine learning techniques and classifiers
• Shallow learning and Deep learning neural network techniques
• Optimisation algorithms for general neural networks
• Unsupervised machine learning techniques
• Introduction to digital signal, image and computer vision fundamentals
• Applications of state-of-the-art supervised and unsupervised machine learning techniques with real datasets

How will I learn on this module?

Each module session follows a similar structure of Learn, Explore Further and Apply. All learning materials and resources are accessible via our virtual learning environment. Indeed, through the e-learning portal you will be provided with resources in the form of articles, links of books/articles/journals, PowerPoint lectures, word document, video lectures etc. relevant to your module. You will be given an on-line reading list, but will also be required to create your individual reading resource as well. You will be using a discussion board to share your work and create a knowledge base for your peers. You will be also using Wiki (a learning tool on e-learning portal) to form focus groups on module submission and assessment criteria.

Formal teaching will take a number of forms. You will learn about concepts, mathematical principles and theories in recorded lectures and these will be demonstrated, practised and discussed in on-line tutorial sessions. The recorded lectures will offer you the opportunity to develop your knowledge of the principles and concepts of different approaches in artificial intelligence.

The subsequent tutorials will be used to reinforce your learning from the lectures through the use of examples and case studies which you will research, discuss and receive feedback from the tutors. These tutorials will enable you to develop your ability to think independently and make judgements on matters relating to artificial intelligence and other machine learning techniques. During tutorial sessions, you will complete/write program code and analyse/solve machine learning 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 artificial intelligence intended to aid your understanding. The rationale for the problem solving tasks during seminars is to support your learning by addressing or finding solutions for real-world scenarios.

The aim is to broaden and deepen your understanding of the theoretical and practical aspects of the subject, which in turn will help broaden your professional skills and abilities.
You will also conduct independent study, as it forms an important element of the module. Independent learning will centre upon identification and pursuit of areas of interest, by providing deeper/broader knowledge and understanding of the subject through a range of learning activities that might include extended reading, reflection, research etc.
You will access virtual classrooms (available on the e-learning portal) for live discussions and virtual taught sessions, which will be recorded and stored on the e-learning portal. These online sessions are timetabled at key points in the module and will deliver relevant knowledge, information and direction for you to fulfil the learning outcomes.

How will I be supported academically on this module?

A range of approaches are adopted to accelerate your learning in this module.

During the first week of this module, you will receive information about the module and Teaching & Learning Plan. The teaching and learning plan (TLP) sets out
• Learning outcomes and overall module and programme aims
• Teaching, learning and assessment strategy
• Teaching schedule
• Directed reading references (text and journals) and core texts for the module

During this module your module tutor will provide academic support including:
• Delivering on—line materials
• Providing guidance in relation to assignments
• Development of key resources, made available through the VLE
• Assessing assignments and assess or review any other agreed summative or formative outputs as appropriate

You will be supported by a team of academic experts and will have the opportunity to discuss your ideas and methods on-line. You will engage in a rich dialogue with tutors (and fellow students) and receive feedback on on-going work giving you the opportunity to respond directly and as part of your process.

Where appropriate, students may also be directed to engage with Study Skills +, or other resources offered through the University Student Support Services such as Dyslexia Support.

The Library is open 24 hours a day and E-Learning Portal houses all your module documents including your timetable. These services can be accessed on a range of devices

The module will also have an e-reading list which directs learners to specific reading for each session. This includes direct access to repositories, journal articles and other academic sources. You will also be provided with access to a significant set academic research sources via the Northumbria University library portal.

You will also have opportunities to receive formative feedback from your tutor in response to opinions you express and issues you raise during workshop sessions and face-to-face or online tutorials. These formative feedback sessions are formally scheduled at key points throughout the module.

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 knowledge and critical understanding of the core artificial intelligence concepts, tools and technologies used in building intelligent systems for solving practical problems
2. Demonstrate appreciation of the artificial intelligence’s contribution to the scientific community
3. Articulate a broad awareness of the ethical aspects of artificial intelligence

Intellectual / Professional skills & abilities:
4. Ability to evaluate available artificial intelligence techniques, tools and technologies and assess their applicability in novel domains
5. Critically analyse and evaluate the effectiveness and efficiency of intelligent systems

Personal Values Attributes (Global / Cultural awareness, Ethics, Curiosity) (PVA):
6. Developing and reinforcing the ethical characteristics of a Northumbria graduate as you consider the values that underpin ethical approaches to applying artificial intelligence and its technologies (the students will have the opportunity to reflect on how these are linked to their own values).

How will I be assessed?

You will write a research report on innovative and emerging AI technologies and applications including an evaluation of an existing AI application / tool and (4000 words). This assignment will assess MLOs 1, 2, 3, 4, 5, 6 (100%)



You will be provided with written, electronic, feedback on the assignment.

Formative assessment and feedback
You will receive guidance and ongoing feedback on your work and progress verbally in lecture and workshop sessions.

You will also have the opportunity to discuss your progress, complete practical’s and the needs of the summative assessment informally in the tutorial sessions.

Pre-requisite(s)

None

Co-requisite(s)

None

Module abstract

This module is designed to provide a general introduction to artificial intelligence and machine learning. Indeed, it will cover fundamental ideas in problem formulation and solving, knowledge representation and machine learning techniques and provide you with the appropriate background necessary to undertake further investigation into advanced and specialized artificial intelligence and machine learning modules.

Specifically, you will learn key theoretical concepts, state-of-the-art techniques and research advances in the field of intelligent systems and upon completing the module, you will have a holistic understanding of how artificial intelligence works in principle and in applications. In particular, you will have a high-level understanding of the artificial intelligence and machine learning techniques coupled with hands-on introductory programming. The module will use case studies to support the tutorials that are developed from research and industrial projects. There will be opportunity throughout the delivery of the module to attend on-line tutorial where you can discuss and practice designing solutions and gain feedback from your module tutors.

Course info

Credits 20

Level of Study Postgraduate

Mode of Study 2 years Distance Learning

Department Computer and Information Sciences

Location City Campus, Northumbria University

City Newcastle

Start Upcoming Intakes - Academic Year 22/23: Oct 22, Jan 23, Apr 23, July 23

Fee Information

Module Information

All information on this course page is accurate at the time of viewing.

Our Campus based courses starting in 2022 and 2023 will be delivered on-campus with supporting online learning content. We continue to monitor government and local authority guidance in relation to Covid-19 and we are ready and able to adjust the delivery of our education accordingly to ensure the health and safety of our students and staff.

On-campus contact time is subject to increase or decrease in line with any additional restrictions, which may be imposed by the Government or the University in the interest of maintaining the health and safety and wellbeing of students, staff, and visitors. This could potentially mean increased or fully online delivery, should such restrictions on in-person contact time be required.

 

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