Module Code: H9AITB
Long Title Artificial Intelligence Technologies for Business 
Title Artificial Intelligence Technologies for Business 
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 10
Module Coordinator: CRISTINA HAVA MUNTEAN
Module Author: Rejwanul Haque
Departments: School of Computing
Specifications of the qualifications and experience required of staff

PhD/Master’s degree in a computing or cognate discipline. May have industry experience also. 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Comprehend and evaluate core AI technologies and the infrastructure required to implement them across different areas of the organisation.
LO2 Critically evaluate the nature and characteristics of data needed for AI in the context of business and the impacts of AI on fairness, trustworthiness, usability, and sustainability.
LO3 Design, evaluate, and communicate AI strategies and governance models in terms of business impact and technical feasibility.
LO4 Comprehend, analyse, and summarise the requirements for the adoption of an AI culture in an organisation.
LO5 Critically review the direction of AI adoption in various business domains addressing concerns and challenges of key stakeholders.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is required before enrolment on this module. While the prior learning is expressed as named NCI module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
No recommendations listed
Co-requisite Modules
No Co-requisite modules listed
Entry requirements

Applicants are required to hold a minimum of a Level 8 honours qualification (2.2 or higher) or equivalent on the National Qualifications Framework in either STEM (e.g., Information Management Systems, Information Technologies, Computer Science, Computer Engineer) or Business (e.g., Business Information Systems, Business Administration, Economics) discipline and a minimum of three years of relevant work experience in industry, ideally but not necessarily, in management. Previous numerical and computer proficiencies should be part of their work experience or formal training. Graduates from disciplines which do not have technical or mathematical problem-solving skills embedded in their programme will need to be able to demonstrate technical or mathematical problem-solving skills in addition to their level 8 programme qualifications (Certifications, Additional Qualifications, Certified Experience and Assessment Tests). All applicants for the programme must provide evidence that they have prior Mathematics and Computing module experience (e.g., via academic transcripts or recognised certification) as demonstrated in one mathematics/statistics module and one computing module or statement of purpose must specify numerical and computing work experience. 

NCI also operates a prior experiential learning policy where graduates with lower, or no formal qualifications, currently working in a relevant field, may be considered for the programme. 

Applicants must also be able to have their own laptop with the minimum required specification that will be communicated to each applicant through both the admissions and marketing departments. 
 

Module Content & Assessment

Indicative Content
Introduction to Artificial Intelligence
History and background of AI • How AI has historically been applied to business What have the successes and concerns been?
Understanding data for AI
• Big data characteristics (e.g., five “V’s” of data, labelled data importance) • Finding, generating, and managing data • Overview on data mining methodologies (e.g., KDD, CRISP-DM) • Dataexploration and visualisation
Artificial Intelligence and Machine Learning
• Types of machine learning (i.e., supervised, semisupervised, reinforcement, and unsupervised learning) • Demonstrate the importance of data preprocessing (cleaning, integration, reduction, and transformation)
Machine Learning and Evaluation of AI models
Basics of predictive modelling (classification, regression) • Model evaluation and the commonly used metrics • Understand the impact of underfitting and overfitting • Understand bias and variance
Artificial Neural Networks and Deep Learning
• Basic building blocks of artificial neural network (e.g., neurons, layers, weights, bias, activation functions, computation) • Introduction to deep learning • Pros and Cons of artificial neural network and deep learning • Main business use cases applying artificial neural network and deep learning • Different use-cases of artificial neural networks and deep learning on various business contexts such as image processing on quality control.
Optimisation and Decision Making for AI
Introduction to optimisation • Differences between optimisation and Case studies of optimisation techniques being applied to simulation (when and where to apply) • Optimisation applied for decision making • Importance of alternative approaches of data-driven models • Pros and cons of optimisation as an alternative approach for data-drive models and what should leaders be aware of.
Infrastructure for AI models
Infrastructure (e.g., hardware, software, platforms) requirements for building and deploying AI models • Steps from data to products; feasibility study for AI models (e.g., latency vs throughput vs scalability vs cost) • Deploying AI models in the cloud • Impact of AI models in business (economic) and in society / environment
Facilitating fair, usable, trusted and green AI
Bias awareness and mitigation • What AI fairness is and the key role it plays in developing AI solutions • Facilitating usable AI development • Facilitating trusted AI development • Emphasize reproducibility
Driving AI for business
How do you design a strategy? • What are the key components? • What is required in an AI Strategy? • How AI relates to the completion of tasks? • What should organisations think of when defining their AI strategy?
AI Governance
• Designing a Governance structure to support AI initiatives • Who is accountable for the AI program? • What does 'accountability' mean? • How are senior managers/the board involved?
Culture to support AI
• What is the culture of the organisation? • What is the culture required to support AI initiatives? • Does it have a culture of Innovation?
Global perspective and the future of AI
Review of some of the leading AI companies from China • The difference between Silicon Valley organisations and Chinese organisations • What does the future of AI look like? Few/Zero Shot Learning Handing lowresource scenarios (both computational and data) Auto ML. Future of deep learning. Quantum Computing
Assessment Breakdown%
Coursework100.00%

Assessments

Full Time

Coursework
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: Yes
Assessment Description:
Formative assessment will be provided on the in-class individual or group activities. Feedback will be provided in written or oral format, or online through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Project % of total: 100
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
First, learners are required to write a project proposal based on how AI can be applied to solve a particular problem in a specific business context. Next, learners will develop an AI strategy that is technically achievable and highly impactful (economic, societal, etc.) identifying the strengths and weaknesses of their strategy in terms of a variety of parameters (e.g., adaptability, robustness). Additionally, the learners should consider different operational issues including ethics, data governance, fairness, stakeholder implications, trustworthiness, costs in terms of business impact versus technical feasibility.
No End of Module Assessment
No Workplace Assessment
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Module Target Workload Hours 0 Hours
 

Module Resources

Recommended Book Resources
  • Thomas H. Davenport. (2019), The AI Advantage, MIT Press, p.244, [ISBN: 978-0262538008].
  • Marco Iansiti,Karim R. Lakhani. Competing in the Age of AI, [ISBN: 978-1633697621].
Supplementary Book Resources
  • David Spiegelhalter. (2019), The Art of Statistics, Basic Books, p.320, [ISBN: 978-1541618510].
  • Kai-Fu Lee. (2018), AI Superpowers, Houghton Mifflin, p.272, [ISBN: 978-1328546395].
  • Bernard Marr. (2019), Artificial Intelligence in Practice, John Wiley & Sons, p.352, [ISBN: 978-1119548980].
Recommended Article/Paper Resources
Supplementary Article/Paper Resources
This module does not have any other resources
Discussion Note: