Long Title:Applied Artificial Intelligence
Language of Instruction:English
Module Code:H8AAI
Credits: 5
NFQ Level:LEVEL 8
Field of Study: Software and applications development and analysis
Module Delivered in 1 programme(s)
Module Coordinator: Vikas Sahni
Module editor: Vikas Sahni
Teaching and Learning Strategy: The teaching and learning strategy involves the use of lectures, tutorials, case studies, class discussions and practical work as appropriate. Lectures will include active learning components such as discussions, demonstrations, and problem-solving and feedback sessions. Practical sessions will comprise of exercises, PBL, group work, and individual self-directed learning. A set of current and relevant artificial intelligence applications will be used as the basis for PBL practical sessions.
Learning Environment: Learning will take place in workplace environment (company) with access to IT resources. Learners will have access to NCI library resources, both physical & electronic and to an academic supervisor where required.
Module Description: The aim of this module is twofold: (1) To introduce students to the principles of Artificial Intelligence and (2) to teach students skills to accomplish practical programming solutions to problems of Artificial Intelligence.
Learning Outcomes
On successful completion of this module the learner will be able to:
LO1 LO 1. Demonstrate a broad knowledge of modern AI theory and applications, including a sense of the successes and failures
LO2 LO 2. Apply advanced AI search techniques
LO3 LO 3. Assess if a problem is amenable for solution by specific AI techniques
LO4 LO 4. Understand supervised and unsupervised machine learning techniques
Pre-requisite learning
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
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 

Module Content & Assessment

Indicative Content
Principles of Applied Artificial Intelligence (20%)
• Logic • Probability theory • Knowledge-based approaches • Neural Networks • Overview of programming languages/tools for AI • Emergent technologies/languages
Subfields of Applied Artificial Intelligence (40%)
• Gaming (e.g., A*, path finding, story generation, AI middleware) • Data Mining (e.g., document classification, evaluation indices) • Machine learning (supervised and unsupervised)
Program Development (40%)
• Finite State Machines • Rule-Based Approaches • Decisions under Uncertainty • Classifier Systems • Performance Evaluation of AI Systems
Assessment Breakdown%
Coursework50.00%
End of Module Assessment50.00%

Full Time

Coursework
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Continuous Assessment (0200) Sample Assessments: Questions which require students to relate principles of Artificial Intelligence to more applied fields (e.g., gaming, data mining, language processing) and evaluate their usage. Weekly laboratory sessions are used to assess students’ knowledge of alternative approaches to address problems of practical Artificial Intelligence and make an informed choice. Lab sessions that require students to evaluate a software solution found on a quantitative level. 2,3,4 50.00 n/a
End of Module Assessment
Assessment Type Assessment Description Outcome addressed % of total Assessment Date
Terminal Exam End-of-Semester Final Examination 1,2,3,4 50.00 End-of-Semester
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
Learners will be afforded an opportunity to repeat the final examination and all learning outcomes will be assessed in the repeat sitting.

NCIRL reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Workload Description Hours Frequency Average Weekly Learner Workload
Lecture No Description 2 Every Week 2.00
Tutorial No Description 1 Every Week 1.00
Independent Learning Time No Description 7.5 Every Week 7.50
Total Hours 10.50
Total Weekly Learner Workload 10.50
Total Weekly Contact Hours 3.00
This module has no Part Time workload.
 

Module Resources

Recommended Book Resources
  • Warwick, Kevin 2011, Artificial Intelligence: The Basics, Routeledge
  • Russell, Stuart & Norvig, Peter 2009, Artificial Intelligence: A modern Approach.,, 3rd Ed., Prentice Hall
Supplementary Book Resources
  • Frankish, Keith & Ramsey, William M 2014, The Cambridge Handbook of Artificial Intelligence, Cambridge University Press
  • Funge, John David, Artificial Intelligence for Computer Games: An Introduction., A K Peters, Wellesley, MA.
This module does not have any article/paper resources
This module does not have any other resources
 

Module Delivered in

Programme Code Programme Semester Delivery
BSHC BSc (Honours) in Computing 8 Optional