Module Code: H8AI
Long Title Artificial Intelligence
Title Artificial Intelligence
Module Level: LEVEL 8
EQF Level: 6
EHEA Level: First Cycle
Credits: 10
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) and outline the historical evolution of AI.
LO2 Evaluate and apply the technical and practical skills for constructing algorithms used in various real-world applications such as natural language processing.
LO3 Demonstrate and evaluate the use of structures for knowledge representation and logical reasoning systems while solving practical AI problems.
LO4 Evaluate the architecture of intelligent agents to solve real world problems.
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 3 of the BSc (Hons) in Computing.
 

Module Content & Assessment

Indicative Content
Introduction to Artificial Intelligence
Foundations of AI: philosophy, maths, psychology, computing, linguistics, logic, probability theory Historical evolution of the field Weak vs Strong AI Ethical implications of AI
Agents
Precepts, actions, goals, environment Rational agents Environments Agent functions and programs Simple reflex agents Reflex agents with state Goal based agents Utility based agents
Search-Based Problem Solving
Utility based agents Performance State space search Uninformed Search strategies: Uniform Cost, DeptFirst, Depth-Limited, Iterative Deepening
Reasoning
Propositional Logic First Order Logic Inference in First Order Logic
Knowledge Representation
Ontological Engineering Categories, objects and events Semantic networks
Bayesian Networks
Quantifying Uncertainty Bayes’ Rules and its use
Natural Language Processing
Language models Retrieval, extraction and classification for natural language processing
Recommender Systems
Recommender systems introduction and examples Basic models of recommender systems
Assessment Breakdown%
Coursework40.00%
End of Module Assessment60.00%

Assessments

Full Time

Coursework
Assessment Type: Formative Assessment % of total: Non-Marked
Assessment Date: n/a Outcome addressed: 1,2,3,4
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: 40
Assessment Date: n/a Outcome addressed: 3,4
Non-Marked: No
Assessment Description:
Project where the students need to implement AI into an application of their choice (e.g., chess game, chat bot, etc.). This project would built on students’ previous skills in programming (e.g. Python), and machine learning.
End of Module Assessment
Assessment Type: Terminal Exam % of total: 60
Assessment Date: End-of-Semester Outcome addressed: 1,2,3,4
Non-Marked: No
Assessment Description:
Written examination held during final terminal exams examining all learning outcomes
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. The repeat strategy for this module is an examination. Students will be afforded an opportunity to repeat the examination at specified times throughout the year and all learning outcomes will be assessed in the repeat exam.

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) 24 Every Week 24.00
Independent Learning Independent learning (hours) 202 Every Week 202.00
Total Weekly Contact Hours 48.00
Workload: Part Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 24 Per Semester 2.00
Tutorial No Description 36 Per Semester 3.00
Independent Learning No Description 190 Per Semester 15.83
Total Weekly Contact Hours 5.00
 

Module Resources

Recommended Book Resources
  • Stuart Russell,Peter Norvig. (2016), Artificial Intelligence, [ISBN: 9781292153964].
  • Hobson Lane,Cole Howard,Hannes Hapke. (2019), Natural Language Processing in Action, Pearson Professional, p.420, [ISBN: 978-1617294631].
  • Dietmar Jannach,Markus Zanker,Alexander Felfernig,Gerhard Friedrich. (2010), Recommender Systems, Cambridge University Press, p.352, [ISBN: 978-0521493369].
  • Timo Koski,John Noble. (2009), Bayesian Networks, Wiley, p.366, [ISBN: 978-0470743041].
Supplementary Book Resources
  • Ian Millington,John Funge. (2009), Artificial Intelligence for Games, CRC Press, p.872, [ISBN: 978-0123747310].
Recommended Article/Paper Resources
This module does not have any other resources
Discussion Note: