SWAGºÏ¼¯

• Dr Irene Moulitsas

Object oriented programming (OOP) is the standard programming methodology used in nearly all fields of major software construction today, including engineering and science and C++ is one of the most heavily employed languages. This module aims to answer the question ‘what is OOP’ and to provide the student with the understanding and skills necessary to write well designed and robust OO programs in C++. Students will learn how to write C++ code that solves problems in the field of computational engineering, particularly focusing on techniques for constructing and solving linear systems and differential equations. Hands-on programming sessions and assignment series of exercises form an essential part of the course. The library support provided for writing C++ programs using a functional programming approach will also be covered.   

An introduction to the Python language is also provided.

• The OOP methodology and method, Classes, abstraction and encapsulation,
• Destructors and memory management, Function and operator overloading, Inheritance and aggregation, Polymorphism and virtual functions, Stream input and output,
• Templates, Exception handling, The C++ Standard Library and STL.

Functional programming in C++.

On successful completion of this module you should be able to:

• Apply the principles of the object oriented programming methodology - abstraction, encapsulation, inheritance and aggregation - when writing C++ programs,
• Design and create robust C++ programs of simple to moderate complexity given a suitable specification,
• Construct C++ programs to solve a range of numerical problems in computational engineering using both OO and functional based approaches, based on the Standard Template Library and other third-party class libraries,
• Design development environments and associated software engineering tools to assist in the construction of robust C++ programs,
• Evaluate existing C++ programs and assess their adherence to good OOP principles and practice.

The importance of technology leadership in driving the technical aspects of an organisation’s products, innovation, programmes, operations and strategy is paramount, especially in today’s turbulent commercial environment with its unprecedented pace of technological development. Demand for ever more complex products and services has become the norm. The challenge for today’s manager is to deal with uncertainty, to allow technological innovation and change to flourish but also to remain within planned parameters of performance. Many organisations engaged with technological innovation struggle to find engineers with the right skills. Specifically, engineers have extensive subject/discipline knowledge but do not understand management processes in organisational context. In addition, STEM graduates often lack interpersonal skills.

• Engineers and Technologists in organisations:
  • the role of organisations and the challenges facing engineers and technologies,
• People management:
  • understanding you, understanding other people, working in teams and dealing with conflicts.
• The Business Environment:
  • understanding the business environment; identifying key trends and their implications for the organisation.
• Strategy and Marketing:
  • developing effective strategies, focusing on the customer, building competitive advantage, the role of strategic assets.
• Finance:
  • profit and loss accounts, balance sheets, cash flow forecasting, project appraisal.
• New product development:
  • commercialising technology, market drivers, time to market, focusing technology, concerns.
• Business game:
  • Working in teams (companies), you will set up and run a technology company and make decisions on investment, R&D funding, operations, marketing and sales strategy,
• Negotiation:
  • preparation for negotiations, negotiation process, win-win solutions.
• Presentation skills:
  • understanding your audience, focusing your message, successful presentations, getting your message across.

On successful completion of this module you should be able to:

• 1. Recognise the importance of teamwork in the performance and success of organisations with particular reference to commercialising technological innovation,
• 2. Operate as an effective team member, recognising the contribution of individuals within the team, and capable of developing team working skills in yourself and others to improve the overall performance of a team,
• 3. Compare and evaluate the impact of the key functional areas (strategy, marketing and finance) on the commercial performance of an organisation, relevant to the manufacture of a product or provision of a technical service,
• 4. Design and deliver an effective presentation that justifies and supports any decisions or recommendations made,
• 5. Argue and defend your judgements through constructive communication and negotiating skills.

The advent of multi-core processors in the commodity desktop computer market has shifted the emphasis from traditional single threaded computing models to more advanced methods in order to take advantage of the additional processing power that is now available. This has implications for both the traditional high performance computing sector and the workstation market. This course aims to explore the different parallel processing techniques now available on small scale computer systems, such as multi-core desktop computers and GPU devices.

Introduction to Parallel and Multi-Threaded Programming, Safety and Liveness: Synchronisation Techniques, OpenMP – concepts, structures and usage.Using CUDA to solve general purpose problems on the GPU, Software Tools (debugging and optimisation).

The aim of this module is to provide you with the necessary knowledge and understanding of virtualisation technologies, cloud anatomies and cloud applications to the provision of on-demand computational resources, as well as a wider understanding of how those resources are consumed through Cloud Computing services. 

Cloud Computing Introduction 

Virtualisation 

Cloud Anatomy 

Cloud Security and Economics 

Applied Clouds (AWS, Google Cloud and MS Azure) 

Distributed Systems 

Data Stream Processing 

Applications of Cloud Computing 

On successful completion of this module you should be able to:

1. Demonstrate understanding and knowledge of key virtualisation technologies, cloud anatomies and their applications to Cloud Computing infrastructure;
2. Identify the characteristics of the Cloud Computing platform and understand the fundamentals of distributed systems and how these Cloud characteristics differ from existing distributed computing environments;
3. Critically assess the characteristics of Cloud Computing technologies, and understand the fundamentals of some key computer science applications (e.g., Machine Learning, Big Data) and how these Cloud characteristics affect the implementation of Cloud based applications;
4. Identify the non-technical challenges that affect the implementation and use of Cloud enabled software;
5. Compare and contrast the suitability of different Cloud Computing approaches to different types of computational problem;
6. Develop and implement Cloud enabled software to solve a specified computational problem.

The aim of this module is to teach you the modern computational skills on a key HPC cluster platform. Many interesting scientific problems require analysis or solution of large data sets. For such problems, harnessing distributed computing and storage resources is clearly of great value. Furthermore, the natural parallelism inherent in many analysis procedures makes it feasible to use distributed resources efficiently. 

• The focus of this module is on parallel algorithms and domain decomposition techniques which are suitable for simulation on High Performance Distributed Computing systems. Emphasis is on algorithms for execution on loosely coupled distributed systems, like grid-systems.
• Data-intensive computing algorithms like distributed data mining and data warehousing.
• Parallel numerical algorithms to solve model applications will be discussed and studied through implementation in the hands-on part of the course.
• Load-balancing methods and domain decomposition techniques will be introduced.

On successful completion of this module you should be able to:

Appraise the need to carry out scientific computing on the HPC platforms and demonstrate relevant critical awareness. 

Formulate elements of parallel program design. 

Relate with the challenges and limitations of the parallelisation process including scalability (Amdahl and Gustafson laws) and load-balancing.  

Create a program to solve a practical problem in scientific computing on the HPC platforms by applying knowledge of intensive computational parallel/distributed techniques. 

Assess the performance and efficiency of a parallel program and propose appropriate code improvements and employ techniques to validate and verify the results of parallel programs.

The Java and Python programming languages have become key languages for developers of computational and scientific codes in both desktop and internet/cloud network based environments.
This module aims to provide you with the necessary skills and knowledge to develop software solutions to problems in these fields using these languages. The principle and advanced elements of Java and Python, associated libraries/tools, programming methodologies and good design principles are covered. Hands-on programming exercises culminating in the construction of a fully functional three-tier application form an essential part of the course.

•    Java and Python program structure and flow, data types, basic and advanced language constructs.
•    Functional and object oriented methodologies.
•    Built-in and third party libraries for computational and scientific software development.
•    Software design principles and practices.
•    Development environments and documentation tools.

The aim of this module is to introduce you to the MapReduce programming model for big data applications where machine learning algorithms are employed. Specific applications will be developed in the Hadoop/Spark frameworks, combined with the implementation of machine learning data analytics techniques in said frameworks.

•    Introduction to data stream processing
•    The MapReduce programming paradigm

o    Implementations: Hadoop and Spark (Python interface)
o    NoSQL data backends (e.g. MongoDB)
o    Other programming interfaces: Scala.
o    Performance optimization techniques.

•    Machine Learning Theory & Methodology
o    Decision Tree Classifiers
o    Instance Based Learning
o    Bayesian Classification
o    Neural Networks
o    Support Vector Machines

•    Application case studies

On successful completion of this module you should be able to:

1. Describe and analyse the architecture of DataStream Processing systems based on Hadoop/Spark
2. Evaluate and assess the applicability of the MapReduce programming paradigm to specific problems
3. Design and implement algorithms for solving data streaming problems in Hadoop/Spark
4. Evaluate the application of machine learning approaches to a wide set of data mining and classification type problems
5. Implement and deploy machine-learning techniques in data stream analysis systems.

To provide an introduction to core Artificial Intelligence concepts, architectures, methods, and tools, and highlight their innovation potential, enabling you to develop a practical knowledge of AI-enabled solutions development processes for product and service innovation.

• AI Concepts
• Paradigms
• Introduction to AI project activity
• Intelligent Agents
• Intelligent Search
• Knowledge Representation
• Logic Programming
• Inference and Reasoning
• Planning
• Learning
• Decision Making and Recommender Systems
• AI Challenges (scalability, security, privacy, ethics)
• AI-driven Innovation in Products and Services

On successful completion of this module you should be able to:

Appraise the main concepts and paradigms of Artificial Intelligence and examine AI-enabled innovation opportunities in different sectors  

Evaluate use cases of theoretical concepts through Artificial Intelligence thinking 

Evaluate key Artificial Intelligence architectures and relate them to use cases. 

Analyse the cognitive, practical and key transferable skills necessary and design solutions for selected Artificial Intelligence topics (intelligent agents, search, logic programming, inference & reasoning, planning and decision making, reinforcement learning, machine learning, and deep learning).  

Assess key challenges in the delivery and take-up of AI-enabled solutions, highlighting the importance of: scalability, security, privacy and ethics. 

This module aims to provide you with skills in the areas of: software development and project management; applications of machine learning and computational intelligence so that you can undertake a group project.

• Project Management (risk analysis, estimation models, project planning and scope definition, communication and team working).

• Software Development.

• Algorithmic stability and performance.

• Specification and performance of computational implementations.

• Validation and tuning of applications on medium-scale distributed architectures.

On successful completion of this module you should be able to:

1. Critically evaluate the project management requirements and compose an effective project plan to outline team roles, project timeline, milestones and communication methods, for the implementation of a software project.

2. Design clear project aims and objectives based on the given problem and select machine learning and computational intelligence methodologies appropriate to the task.

3. Formulate appropriate advanced programming paradigms in the development of the relevant machine learning and computational intelligence algorithms and examine the use of automated tools to assist the software development process.

4. Critically appraise individual skills and expertise to contribute to team output and collaborate effectively with others in a consultancy team context. Reflect upon and evaluate personal and group performance in a team-based task.

5. Communicate research findings in a professional manner in written, oral and visual forms.

This module allows you to understand the data visualisation principles and techniques to effectively visualise algorithms, data sets, and complex systems. It aims to build on the concepts and issues of working with data and presenting data using appropriate approaches and visualisations, combined with the implementation of data analytics techniques in the framework.

• Introduction to visualisation.

• Principles and techniques for creating effective visualisations.

• Limits of visualisation and uncertainty.

• Visualising data sets, e.g. text based and numeric data sets.

• Visualising algorithms, processes and systems, e.g. sampling algorithms, sorting algorithms, Machine Learning and Deep Learning algorithms, and cellular automata.

• Interactive visualisation.

• Python as a tool for visualising information.

On successful completion of this module you should be able to:

1. Critically analyse the core skills required to evaluate algorithms, data sets, complex systems and processes, incorporating both traditional and emerging techniques such as interactive visualisations, real-time data rendering and visualisations.

2. Appraise the use of complex procedures, data patterns, and emergent systems through a systematic approach, employing techniques such as network analysis, temporal data visualisation, geospatial data visualisation, and hierarchical visualisations to uncover hidden insights in the data.

3. Assess visualisation needs and be able to select and apply an appropriate range of visualisation and reporting techniques to data sets relevant to the specific domain.

4. Apply data analytics techniques for visualisation implementation and interpret the visualisation results.

5. Evaluate visualisations in terms of different users and tasks with human factors and critically appraise visualisation approaches.