This module is a core module for the MSc in Artificial Intelligence. The main objective of this module is to prepare students to understand and use a wealth of new technologies around the management of data, the sharing, composition, reuse, and retrieval of algorithms, optimisation techniques and data pertaining to the production and use of Artificial Intelligence and Data Science.

1. Software and data ecosystems: a system-level view. 2. Syntactic and semantic characterization of algorithms and data. 3. Computation platforms, software integration, composability along data and control structures. 4. Principles of problem modelling for optimization 5. Resource allocation problems as optimization problems 6. Modelling alternatives for Data Science, e.g., Constraint Satisfaction, Boolean Satisfiability, Linear and Integer Linear Programming, Quadratic / Non-Linear Programming 7. Modelling languages / environments 8. Process and workflow approaches to describe and manage complex scientific computations (exemplified on case studies). 9. Knowledge management: properties, ontologies, shared and open access resources. 10. Advanced applications, e.g . automatic compliance checking, automatic workflow synthesis, machine-supported correctness, compliance and security enforcement, system evolution.

Upon Successful Completion of this module students will be able to: 1. Demonstrate an ability to formulate machine learning and other data exploration algorithms as optimization algorithms; 2. Demonstrate an ability to formulate resource allocation problems in one, or more, optimization modelling environments 3. Understand the system-level interplay of collaborating tools and platforms that co-deliver a complex AI-enhanced application. 4. 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 5. Apply the criteria to scientific computing tools, programming languages and host platforms.

Upon Successful Completion of this module students will be able to: 1. Instill awareness of societal implications of decision making algorithms. 2. Understand and formulate rigorous qualities of systems: the "ethics" of the responsibility for quality. 3. Demonstrate a professional commitment to ethics in the practice of data manipulation and analysis. 4. Know the criteria and the mindset behind the requirements and the delivery of "assurance" and "quality", especially in face of complex ecosystems that deliver AI-enhancement or support. 5. Undertake and reflect on interaction (discussion, collaboration and agreement) and teamwork in the practical components of the module 

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The module will be delivered using a blended learning approach using on-line lectures, tutorials and case studies.