| Module Code: |
H9EEAI |
|
Long Title
|
Engineering and Evaluating Artificial Intelligence Systems
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|
Title
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Engineering and Evaluating Artificial Intelligence Systems
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| Module Level: |
LEVEL 9 |
| EQF Level: |
7 |
| EHEA Level: |
Second Cycle |
| Module Coordinator: |
Rejwanul Haque |
| Module Author: |
Shauni Hegarty |
| Departments: |
School of Computing
|
| Specifications of the qualifications and experience required of staff |
|
| Learning Outcomes |
| On successful completion of this module the learner will be able to: |
| # |
Learning Outcome Description |
| LO1 |
Comprehend, contrast, assess, and apply software architecture principles in the design of AI systems. |
| LO2 |
Theoretically evaluate the AI systems in terms of completeness, complexity, and admissibility. |
| LO3 |
Evaluate, summarise, critique, and present the quality and performance of AI systems. |
| LO4 |
Determine and critique the infrastructure for the deployment of AI systems. |
| 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
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| No Co-requisite modules listed |
Module Content & Assessment
| Indicative Content |
|
Artificial Intelligence Engineering
•Overview of AI Engineering•Software Development Life Cycle•Agile Development for AI
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Software Architecture
Software Architecture•Architectural Structures and Views•What Makes a "Good" Architecture?•Importance of Software Architecture•Contexts of Software Architecture•Understanding Quality Attributes•Specifying Quality Attribute Requirements•Quality Attributes (i.e., Availability, Interoperability, Modifiability, Performance, Security, Testability, Usability, Deployability, Energy, and Safety)
|
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Architectural Solutions
•Architectural Patterns•Architecting in the Cloud•Architecture for Machine Learning and Artificial Intelligence
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Designing the Architecture
•Architecture in Agile Projects•Design Strategy•Attributed-Driven Design (ADD) Method•Steps of ADD
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Documenting Architecture
•Notation for Architecture Documentation•Views•Documenting Behaviour•Architecture Documentation and Quality Attributes•Documenting in Agile Projects
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Architecture Evaluation
•Evaluation Factors•The Architecture Trade-off Analysis Method•Lightweight Architecture Evaluation
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Computational Complexity
•Basic Conventions•Big O Notation•Deterministic and Non-Deterministic Models of Computation•Class P, NP and NP Complete•Coping with NP Hardness•Time and Space Complexity
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Evaluating Algorithms
•Admissibility of a Heuristic•Completeness•Evaluating Algorithm Correctness•Sensitivity Analysis
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Software Quality Assurance
•Software Quality Characteristics•Software Testing Life Cycle•Types of Testing Techniques (e.g., Black-Box Testing, White-Box Testing)
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Deployment Pipeline
•Introduction to DevOps•Building and Testing•Deployment Strategies
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Infrastructure Design
•Infrastructure Design, Testing, and Reuse•Modularity and Infrastructure Churn•Scalability•Distributed System
|
| Assessment Breakdown | % |
|---|
| Coursework | 50.00% |
| End of Module Assessment | 50.00% |
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. |
|
| Assessment Type: |
Continuous Assessment |
% of total: |
50 |
| Assessment Date: |
n/a |
Outcome addressed: |
1,3 |
| Non-Marked: |
No |
Assessment Description:
This assessment will evaluate the learners’ comprehension of software architecture principles and skills for applying that knowledge to design AI systems. Learners will be provided with a description of an AI system or case study. Learners will be required to identify the minimum architectural and software quality requirements, design and document a software architecture for the proposed AI system. |
|
| End of Module Assessment |
| Assessment Type: |
Terminal Exam |
% of total: |
50 |
| Assessment Date: |
End-of-Semester |
Outcome addressed: |
1,2,3,4 |
| Non-Marked: |
No |
Assessment Description:
The examination will be of two hours duration and may include a mix of: theoretical, applied and interpretation questions. |
|
| 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.
|
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 |
Lectures |
24 |
Per Semester |
2.00 |
| Independent Learning |
Independent Learning |
89 |
Per Semester |
7.42 |
| Tutorial |
Tutorials |
12 |
Per Semester |
1.00 |
| Total Weekly Contact Hours |
3.00 |
Module Resources
| Recommended Book Resources |
|---|
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Bass, L., Clements, P., & Kazman, R. (2022). Software Architecture in Practice(4th ed.). Addison-Wesley Professional. SEI Series in Software Engineering. [ISBN: 978-0136886099].
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Bass, L., Weber, I., & Zhu, L. (2016). DevOps: A Software Architect’s Perspective. Addison-Wesley Professional. SEI Series in Software Engineering. [ISBN: 978-9332570375].
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Mahfuz, A. S. (2016). Software Quality Assurance: Integrating Testing, Security, and Audit. Auerbach Publications. [ISBN: 978-1498735537].
| | Supplementary Book Resources |
|---|
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Arora, S. & Barak, B. (2009). Computational Complexity: A Modern Approach. Cambridge University Press. [ISBN: 978-0521424264].
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Cervantes, H. & Kazman, R. (2016). Designing Software Architectures: A Practical Approach. Addison-Wesley Professional. [ISBN: 978-0134390789].
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Hulten, G. (2018). Building Intelligent Systems: A Guide to Machine Learning Engineering. Apress. [ISBN: 978-1484234310].
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Humble, J. & Farley, D. (2010). Continuous Delivery: Reliable Software Releases through Build, Test, and Deployment Automation. Addison-Wesley Professional.[ISBN: 978-0321601919].
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Richards, M. & Ford, N. (2020). Fundamentals of Software Architecture: An Engineering Approach. O’Reilly. [ISBN: 978-1492043454].
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Tarlinder, A. (2016). Developer Testing: Building Quality into Software. Addison-Wesley Professional.[ISBN: 978-0134291062].
| | This module does not have any article/paper resources |
|---|
| This module does not have any other resources |
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