Module Code: |
H8AI |
Long Title
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Artificial Intelligence
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Title
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Artificial Intelligence
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Module Level: |
LEVEL 8 |
EQF Level: |
6 |
EHEA Level: |
First Cycle |
Module Author: |
Alex Courtney |
Departments: |
School of Computing
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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.
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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).
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No recommendations listed |
Co-requisite Modules
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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.
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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
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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
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Search-Based Problem Solving
Utility based agents
Performance
State space search
Uninformed Search strategies: Uniform Cost, DeptFirst, Depth-Limited, Iterative Deepening
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Reasoning
Propositional Logic
First Order Logic
Inference in First Order Logic
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Knowledge Representation
Ontological Engineering
Categories, objects and events
Semantic networks
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Bayesian Networks
Quantifying Uncertainty
Bayes’ Rules and its use
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Natural Language Processing
Language models
Retrieval, extraction and classification for natural
language processing
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Recommender Systems
Recommender systems introduction and examples
Basic models of recommender systems
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Assessment Breakdown | % |
Coursework | 40.00% |
End of Module Assessment | 60.00% |
AssessmentsFull 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. |
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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. |
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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 |
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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.
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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.
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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 |
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Stuart Russell,Peter Norvig. (2016), Artificial Intelligence, [ISBN: 9781292153964].
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Hobson Lane,Cole Howard,Hannes Hapke. (2019), Natural Language Processing in Action, Pearson Professional, p.420, [ISBN: 978-1617294631].
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Dietmar Jannach,Markus Zanker,Alexander Felfernig,Gerhard Friedrich. (2010), Recommender Systems, Cambridge University Press, p.352, [ISBN: 978-0521493369].
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Timo Koski,John Noble. (2009), Bayesian Networks, Wiley, p.366, [ISBN: 978-0470743041].
| Supplementary Book Resources |
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Ian Millington,John Funge. (2009), Artificial Intelligence for Games, CRC Press, p.872, [ISBN: 978-0123747310].
| Recommended Article/Paper Resources |
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-
Gabrani G., Sabharwal S., Singh V.K. (2017), Artificial Intelligence Based
Recommender Systems: A Survey, Advances in Computing and Data Sciences,
| This module does not have any other resources |
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