Module Code: H7BAI
Long Title Business and Artificial Intelligence
Title Business and Artificial Intelligence
Module Level: LEVEL 7
EQF Level: 6
EHEA Level: First Cycle
Credits: 5
Module Coordinator:  
Module Author: Alex Courtney
Departments: School of Computing
Specifications of the qualifications and experience required of staff


MSc and/or PhD degree in computer science 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 Describe the theory and concepts underpinning Artificial Intelligence (AI), as well as discuss the seminal and current applications of AI
LO2 Develop a high-level understanding of the key techniques used in AI
LO3 Identify problems in industry which AI can be used to solve, and propose appropriate solutions to these problems
LO4 Review state of the art AI tools, systems and publications
LO5 Assess the implications of implementing AI systems
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

Learners should have attained the knowledge, skills and competence gained from stage 2 of the BSc (Hons) in Computer Science
 

Module Content & Assessment

Indicative Content
Introduction to AI
Foundations of AI: philosophy, maths, psychology, computing, linguistics, logic, probability theory. Historical evolution of the field. Weak vs Strong AI
Agents
Percepts, actions, goals, environment. Simple reflex agents. Reflex agents with state. Goal based agents. Utility based agents
Search Strategies
Uninformed Search strategies: Uniform Cost, Breadth-First, Depth-First. Informed Search strategies: Greedy Best First Search, A* Search, Heuristic functions
Selected Topics in AI (I)
High-level overview and Applications of AI Techniques such as Mathematical Optimization, Machine Learning, Natural Language Processing
Selected Topics in AI (II)
High-level overview and Applications of AI Techniques such as Recommender Systems, Deep Learning, Computer Vision and Knowledge Representation
Employing AI in Business (I)
Embedding AI into business processes: AI in Education, AI in Finance
Employing AI in Business (II)
Embedding AI into business processes: AI in Agriculture, AI in Marketing
Employing AI in Business (III)
Embedding AI into business processes: AI in Manufacturing
Re-imagining Processes with AI (I)
Developing and deploying responsible AI. Improving productivity with AI
Re-imagining Processes with AI (II)
Human and Machine Collaboration
Implications of AI (I)
Ethics of AI. Impact on Decision Making
Implications of AI (II)
Impact on Organisations. Impact on Society (i.e. employment, income, human-computer relationships
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%

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.
Assessment Type: Project % of total: 50
Assessment Date: n/a Outcome addressed: 3,4
Non-Marked: No
Assessment Description:
Learners should search for several interesting examples of where AI is being applied, and prepare a report and presentation on these applications. An overview of the techniques, novel contributions, strengths, weaknesses, limitations and opportunities of the technologies applied should be covered. A current opportunity/problem should also be identified, and a strategy for implementing an AI solution is documented. Limitations of proposed solution should also be discussed.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 50
Assessment Date: End-of-Semester Outcome addressed: 1,2,5
Non-Marked: No
Assessment Description:
The end of semester examination will contain questions on concepts, techniques, applications and implications of AI. Marks will be awarded based on clarity, structure, relevant examples, depth of topic knowledge and an understanding of the potential and limits of solutions.
No Workplace Assessment
Reassessment Requirement
Repeat examination
Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.
Reassessment Description
Repeat examination Reassessment of this module will consist of a repeat examination. It is possible that there will also be a requirement to be reassessed in a coursework element.

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

 

Module Workload

Module Target Workload Hours 0 Hours
Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 24 Every Week 24.00
Tutorial Other hours (Practical/Tutorial) 12 Every Week 12.00
Independent Learning Independent learning (hours) 89 Every Week 89.00
Total Weekly Contact Hours 36.00
 

Module Resources

Recommended Book Resources
  • !!!Book Not Found, [ISBN: 978-1633693869].
  • Rajendra Akerkar. (2018), Artificial Intelligence for Business, Springer, p.81, [ISBN: 978-3319974354].
  • Kartik Hosanagar. (2019), A Human's Guide to Machine Intelligence, Penguin, p.272, [ISBN: 9780525560890].
  • Yeonjoo Lee, Miyeon Ha, Sujeong Kwon, Yealin Shim, Jinwoo Kim.. (2019), , Egoistic and altruistic motivation: How to induce users’ willingness to help for imperfect AI, Computers in Human Behavior, n/a, https://doi, org/10.
  • Roger Clarke.. (2019), , Principles and business processes for responsible AI, Computer Law & Security Review, n/a, https://doi, org/10.
Supplementary Book Resources
  • Stuart Russell,Peter Norvig. (2016), Artificial Intelligence: A Modern Approach, Global Edition, Pearson Higher Ed, p.1152, [ISBN: 1292153970].
  • Article/Paper List.
  • Type.
  • Item.
This module does not have any article/paper resources
This module does not have any other resources
Discussion Note: