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Parent Programme
BSc in Computing Science Level 7 NFQ
NFQ Level & Reference
Level 7 / Ref M1.12
12 Weeks X 3 Hours per week
Artificial Intelligence Basics
Module Credit Units

Artificial Intelligence Basics

Introduction to AI

This Artificial Intelligence Basics course aims to provide learners with a solid foundation in the field of Artificial Intelligence (AI), enabling them to grasp key concepts, methodologies, and applications. It focuses on demystifying AI for a diverse audience, fostering a deeper understanding of its principles and implications.

It will equip learners with the knowledge and skills necessary to navigate the rapidly evolving landscape of AI, empowering them to contribute meaningfully to discussions, projects, and developments in this dynamic field.

  • It will cultivate an awareness of AI's potential societal and economic implications, including its role in shaping the future job market.
  • Enhance problem-solving abilities by applying AI techniques to address practical challenges.
  • Prepare participants for further exploration in specialized AI domains through a solid understanding of foundational principles.

Indicative Syllabus Content

History of AI: (10%)

  • What is AI and what it is not. Rationality
  • AI origins. Turing, McCulloch & Pitts, Minsky & McCarthy etc
  • AI major milestones.

Main Pillars of AI - theory & practice (75%)

  • Expert Systems
  • Machine Learning (Supervised, unsupervised, reinforcement learning)
  • Natural Language Processing (Classification, machine translation)
  • Perception: Computer Vision (image recognition and machine vision)
  • Speech (text to speech and speech to text)
  • Planning
  • Robotics
  • Multi-agent systems

Ethical Considerations in AI (15%)

  • Hone critical thinking skills to evaluate the strengths and limitations of AI applications.
  • Encourage a well-rounded perspective on the societal and technological implications of AI.
  • Delve into the ethical implications of AI, examining issues such as bias, privacy, and responsible AI development.

Minimum Intended Learning Outcomes (MIMLOs)

Upon successful completion of this module, the learner should be able to:
Know the history of AI, key influences and evolutions that have brought us to where we are today.
Discuss the established approaches in AI.
AI in practice, applying the theories learned in real world situations.
Ethical Considerations in AI.


1, 2, 3, 4
CA 1, 3, 5, 7 & 9 - Written Assignment
Total 100%
1, 2, 3, 4
CA 2, 4, 6, 8 & 10 - Practical Assignment
All assessments
Assessments Every fortnight

Reassessment Opportunity

Where the combined marks of the assessment and examination do not reach the pass mark the learner will be required to repeat the element of assessment that they failed. Reassessment materials will be published on Moodle after the Examination Board and will be aligned to the MIMLOs and learners will be capped at 40% unless there are personal mitigating circumstances.

Aims & Objectives

  • Understand the fundamental concepts of AI, including machine learning, neural networks, and natural language processing.
  • Explore the historical development of AI and its current state, with a particular emphasis on contemporary applications.
  • Develop critical thinking skills to evaluate ethical considerations surrounding AI technologies.
  • Gain practical insights into implementing AI algorithms through hands-on exercises and case studies
  • Analyse real-world examples of AI applications across various industries, examining their impact on society.
  • Acquire a working knowledge of AI tools and programming languages commonly used in the field.
  • Comprehend definite and indefinite integrals for problem-solving.
  • Utilize vectors for mathematical operations and geometric transformations.
  • Understand linear maps, solving equations, and matrix transformations.