Module: Foundations of Artificial Intelligence

Latest Module Specifications

Current Academic Year 2025 - 2026

Module Title	Foundations of Artificial Intelligence
Module Code	CSC1147 (ITS: CA686)
Faculty	Computing	School	Engineering & Computing
NFQ level	9	Credit Rating	7.5

Description

This module will provide students with a theoretical and practical grounding in the most important foundational topics in Artificial Intelligence, e.g. AI as search, problem solving, machine learning, machine evolution, perceiving and acting.

Learning Outcomes

1. Explain the basic concepts of artificial intelligence, including its philosophical foundations, and present status and future developments.
2. Implement and evaluate search techniques in AI, including constraint satisfaction, heuristic search and adversarial search.
3. Implement and evaluate computational evolution techniques in AI, including genetic algorithms to solve large-dimensional problems.
4. Implement and evaluate machine learning techniques in AI, including some or all of supervised learning, neural networks, knowledge in learning, probabilistic learning, and reinforcement learning.

*Type*	*Hours*	*Description*
Workload	Full time hours per semester
Lecture	36	No Description
Assignment Completion	24	No Description
Independent Study	127.5	No Description
Total Workload: 187.5

Section Breakdown
CRN	10638	Part of Term	Semester 1
Coursework	0%	Examination Weight	0%
Grade Scale	40PASS	Pass Both Elements	Y
Resit Category	RC1	Best Mark	N
Module Co-ordinator	Mark Humphrys	Module Teacher	Hemant Kumar Singh

Type	Description	% of total	Assessment Date
Assessment Breakdown
Laboratory Portfolio	Complete a series of laboratory exercises and assignments to gain experience of the methods introduced in formal teaching, and to demonstrate that the learning objectives have been met.	40%	n/a
Formal Examination	3 hour examination	60%	End-of-Semester

Reassessment Requirement Type
Resit arrangements are explained by the following categories; RC1: A resit is available for both^* components of the module. RC2: No resit is available for a 100% coursework module. RC3: No resit is available for the coursework component where there is a coursework and summative examination element. ^* ‘Both’ is used in the context of the module having a coursework/summative examination split; where the module is 100% coursework, there will also be a resit of the assessment

Pre-requisite	None
Co-requisite	None
Compatibles	None
Incompatibles	None

All module information is indicative and subject to change. For further information,students are advised to refer to the University's Marks and Standards and Programme Specific Regulations at: http://www.dcu.ie/registry/examinations/index.shtml

Indicative Content and Learning Activities

Introduction to Artificial Intelligence
Brief history of AI, survey of AI main application fields. The state of the art in modern AI including notable applications and successes. Survey of philosophy of AI and its relation to cognitive science. Open issues in AI.

Problem solving in AI
The paradigm of AI problem solving as search in a large statespace. Exhaustive search. Heuristic search. Search strategies (informed and non). Games, constraint satisfaction problems.

Search - Finding satisfactory paths
Depth-first and breadth-first, iterative deepening, local search and heuristic search. Finding optimal paths: branch and bound, dynamic programming, A*. Adversarial search.

Machine evolution
Computational evolution. Genetic algorithms. Codings of genotype. Fitness and reproduction algorithms. Dynamic fitness function. Large dimensional search. Genotype and phenotype.

Machine Learning
Supervised learning. Neural networks. Back propagation. Coding of inputs. Learning policy. Re-play of experiences. Deep learning. Reinforcement learning. Applications of machine learning.

Indicative Reading List

Books:

Stuart Russell, Peter Norvig: 2016, Artificial Intelligence: A Modern Approach, 3, Pearson, 978-129215396

Articles:
None

Other Resources

None