Module Code: H9FAI
Long Title Foundations of Artificial Intelligence
Title Foundations of AI
Module Level: LEVEL 9
EQF Level: 7
EHEA Level: Second Cycle
Credits: 5
Module Coordinator: Arghir Moldovan
Module Author: Arghir Moldovan
Departments: School of Computing
Specifications of the qualifications and experience required of staff

MSc and/or PhD degree in computer science or cognate discipline. Experience lecturing in the field. May have industry experience also.

Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Demonstrate critical understanding of the theory and concepts underpinning Artificial Intelligence.
LO2 Critically analyse the capabilities and limitations of current AI techniques.
LO3 Evaluate and apply the principles, models and algorithms of AI to recognise, model and solve various problems.
LO4 Review, compare, and contrast the latest AI industry and research developments.
LO5 Develop and enhance interpersonal communication skills to become a successful member of a working team.
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

Internal to the programme.


Module Content & Assessment

Indicative Content
Introduction to Artificial Intelligence
Defining Artificial Intelligence; Foundations of AI: philosophy, maths, psychology, computing, linguistics, logic, probability theory; Historical evolution of the field.
Types of AI
Weak vs. strong AI; Narrow vs. general vs. super intelligence; Classical vs. statistical AI; Use cases and examples of applications for the different types of AI.
Business applications of AI
The opportunities and challenges of AI for business; Building safe, ethical, explainable and responsible AI applications; Use cases and examples of AI applications in business.
Precepts, actions, goals, environment; Rational agents; Environments representation; Agent functions and programs; Types of agents: simple reflex agents, reflex agents with state, goal based agents, utility based agents.
Solving Problems by Searching
Defining search problems; Searching for solutions; Performance; Uninformed search strategies; Informed Search strategies; Heuristic functions.
Solving Problems by Searching
Local search algorithms and optimisation problems; Adversarial search.
Propositional Logic; First Order Logic; Inference in First Order Logic; Planning.
Knowledge Representation
Ontological Engineering; Categories and objects; Events; Semantic networks.
Bayesian Networks
Quantifying uncertainty; Bayes’ rules and its use; The semantics of Bayesian networks; Conditional distributions and efficient representation of them; Inference in Bayesian networks.
Types of learning (e.g., deductive vs. inductive, supervised vs. unsupervised vs. semi-supervised, reinforcement learning); Learning from examples (e.g., regression, classification); Brief overview of artificial neural networks; Learning probabilistic models.
Reinforcement Learning
Formulating a reinforcement learning problem; Classification of reinforcement learning algorithms (e.g., model-free vs. model-based); Markov Decision Processes; Value functions; Reinforcement learning examples.
Recommended Systems
Recommender systems types and models (e.g., collaborative filtering, content-based filtering, multi-criteria systems, etc.); Recommender systems challenges (e.g., scalability, sparsity, cold start, etc.); AI techniques applied to improve accuracy and overcome challenges with rec sys; Overview of commercial and public cloud AI services for recommendation (e.g., Amazon Personalize, Microsoft Personalizer, Google Cloud Recommendations AI, Coveo Relevance Cloud, Yusp, etc.). Recommender systems examples and case studies including ethical implications (e.g., Netflix Prize data anonymisation and legal case).
Assessment Breakdown%
End of Module Assessment40.00%


Full Time

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 on-line through Moodle. In addition, in class discussions will be undertaken as part of the practical approach to learning.
Assessment Type: Project % of total: 60
Assessment Date: n/a Outcome addressed: 1,2,3,4,5
Non-Marked: No
Assessment Description:
In this project students will have to propose an Artificial Intelligence-based application, appraise the business value and innovation of the application, analyse, and define the application requirements, document, and justify the use of AI principles, models, and algorithms. Students will work in groups and will also have to consider the ethical implications of their project will be evaluated as part of the Data Governance and Ethics module. The final submission will consist of a written report documenting the work.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 40
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
The examination will be of two hours duration and may include a mix of: theoretical, applied and interpretation questions.
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
The reassessment strategy for this module is by repeat examination that covers all learning outcomes.

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 Per Semester 2.00
Tutorial Practical/Tutorial 12 Per Semester 1.00
Independent Learning Independent learning 89 Per Semester 7.42
Total Weekly Contact Hours 3.00
Workload: Blended
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture Classroom & Demonstrations (hours) 12 Per Semester 1.00
Tutorial Practical/Tutorial 12 Per Semester 1.00
Directed Learning Directed learning 12 Per Semester 1.00
Independent Learning Independent learning 89 Per Semester 7.42
Total Weekly Contact Hours 3.00

Module Resources

Recommended Book Resources
  • Stuart Russell, Peter Norvig. (2020), Artificial Intelligence, 4. Pearson Higher Education, p.1136, [ISBN: 978-0134610993].
Supplementary Book Resources
  • Andrew Burgess. (2018), The Executive Guide to Artificial Intelligence: How to Identify and Implement Applications for AI in Your Organization, 1. Palgrave Macmillan, p.181, [ISBN: 978-3319638195].
  • Steven Finlay. (2021), Artificial Intelligence and Machine Learning for Business: A No-Nonsense Guide to Data Driven Technologies, 4. Relativistic, p.226, [ISBN: 978-1999325381].
  • Mariya Yao,Adelyn Zhou,Marlene Jia. (2018), Applied Artificial Intelligence: A Handbook For Business Leaders, TOPBOTS, p.246, [ISBN: 978-0998289021].
This module does not have any article/paper resources
Other Resources
Discussion Note: