ENGI4607: Artificial Intelligence and Deep Learning

• MATH1551 Maths for Engineers and Scientists

• None

• None

• This module is designed solely for students studying Department of Engineering degree programmes.
• To provide underlying methodological and practical knowledge in the field of Artificial Intelligence (AI), machine learning and deep learning, covering a wide range of the modelling and computational techniques ubiquitous in real-world problems in companies, healthcare and bio-related applications, academia or the third sector.
• To enable students to approach complex ill-defined problems that require deep layers of learning, and understand how this relates to learning in nature.
• To equip students with the necessary knowledge and skills to work in the field of AI and to contribute to ongoing research in the area.
• To make students aware of best practices for fair and equitable use of AI.

• Introduction of AI and Machine Learning
• Machine Learning Basics
• Classical Supervised Learning Algorithms
• Classical Clustering Algorithms
• Dimensionality Reduction (Feature Extraction) methods
• Deep Neural Networks
• Recurrent Neural Networks
• Autoencoder
• Generative Models
• Transfer Learning and fine tuning
• Review of some advanced methods
• Ethical and Bias Issues

Subject-specific Knowledge:

• The key principles of machine learning managing datasets and building models and core methodologies in relation to managing data and training models.
• An understanding of state-of-the-art deep neural network architectures and neural network architecture components.
• An understanding of the algorithms and approaches to design and evaluate deep neural networks.
• The key principles of ethical concerns and bias in AI for real-world applications, from an accountability perspective and as regards the application of algorithms (Ethics and Bias in AI).
• AHEP4 Learning Outcomes: In order to satisfy Professional Engineering Institution (PEI) accreditation requirements the following Accreditation of Higher Education Programmes (AHEP4) Learning Outcomes are assessed within this module:
• M1. Apply a comprehensive knowledge of mathematics, statistics, natural science and engineering principles to the solution of complex problems (coursework assessed).
• M2. Formulate and analyse complex problems to reach substantiated conclusions (coursework assessed).
• M3. Select and apply appropriate computational and analytical techniques to model complex problems, discussing the limitations of the techniques employed (coursework assessed).

Subject-specific Skills:

• An ability to manage data and to select and apply appropriate algorithms to recognise patterns within the data, together with an ability to implement, analyse and compare learning algorithms.
• An ability to use modern deep learning libraries to design, train, validate and test deep neural networks.
• An ability to design appropriate neural network architectures suited for a given task or dataset.
• An ability to discuss implications of AI solutions in real-world applications and applying sensitivity analysis to given data (Ethics and Bias in AI).

Key Skills:

• An ability to communicate technical information in the domain of artificial intelligence and machine learning .
• An ability to learn, understand, and visualise the underlying structure of datasets.
• An ability to design and implement state-of-the-art machine learning models.
• The scientific approach to design, training, validation, and testing of deep neural networks in a broad range of applications.
• An ability to appreciate positive and negative societal impact of AI.

• The module content is delivered in lectures and is reinforced by problem sheets, equipping students with the required problem-solving capability.
• Practical classes enable the students to put into practice learning from lectures and strengthen their understanding through application.
• Students are encouraged to make use of staff 'Surgeries' (otherwise "Office Hours") to discuss any aspect of the module with teaching staff on a one-to-one basis. These are sign-up sessions available for up to one hour per week.
• Formative and summative assignments encourage and guide independent study, and test the knowledge acquired and the students' ability to use this knowledge to solve problems.

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