Module Code - Title:

CS5024 - THEORY AND PRACTICE OF ADVANCED AI ECOSYSTEMS

Year Last Offered:

2024/5

Hours Per Week:

Lecture

2

Lab

0

Tutorial

1

Other

3

Private

4

Credits

6

Grading Type:

N

Prerequisite Modules:

Rationale and Purpose of the Module:

This module is a core module for the MSc in Artificial Intelligence. The aim is to prepare students to understand and use a wealth of new technologies around the sharing, composition, reuse, and retrieval of algorithms and data pertaining to the production and use of Artificial Intelligence as well as the guarantees of system-level correctness, compliance and quality.

Syllabus:

1. Software and data ecosystems: a system-level view. 2. Syntactic and semantic characterization of algorithms and data. 3. Computation platforms, composability along data and control structures. 4. Theoretical aspects: equivalence, congruence, process models, property expression languages; differene between checking and synthesis. 5. Assume-guarantee approach to system description, composition, and property checking. 6. Process and workflow approaches to describe and manage complex scientific computations (exemplified on AI, ML or Data Analytics case studies). 7. Knowledge management: properties, ontologies, shared and open access resources. 8. Advanced applications, e.g . automatic compliance checking, automatic workflow synthesis, machine-supported correctness, compliance and security enforcement, system evolution. 9. Application to case studies (individual or group project).

Learning Outcomes:

Cognitive (Knowledge, Understanding, Application, Analysis, Evaluation, Synthesis)

1. Understand the system-level interplay of collaborating tools and platforms that co-deliver a complex AI-enhanced application. 2. Analyse and evaluate workflows and processes that use Artificial Intelligence-related tools and techniques with respect to the criteria introduced in the theoretical part of the module 3. Apply the criteria to scientific computing tools, programming languages and host platforms introduced in previous modules. 4. Know the criteria and the mindset behind the requirements and the delivery of "assurance" and "quality", especially in face of heterogeneity of components, technologies and platforms in complex ecosystems that deliver AI-enhancement or support. 5. Application of the learned concepts, tools and techniques to a project.

Affective (Attitudes and Values)

1. Understand and formulate rigorous qualities of systems: the "ethics" of the responsibility for quality. 2. Undertake and reflect on interaction (discussion, collaboration and agreement) and teamwork in the practical components of the module.

Psychomotor (Physical Skills)

N/A

How the Module will be Taught and what will be the Learning Experiences of the Students:

The module will be delivered using a blended learning approach using on-line lectures, labs and tutorials. A project will support the practical part.

Research Findings Incorporated in to the Syllabus (If Relevant):

Prime Texts:

Other Relevant Texts:

Andriy Burkov (2020) Machine Learning Engineering , True Positive Inc.

Programme(s) in which this Module is Offered:

Semester(s) Module is Offered:

Spring

Module Leader:

Patrick.Denny@ul.ie