Durham University
Programme and Module Handbook

Undergraduate Programme and Module Handbook 2023-2024 (archived)

Module ENGI4607: Artificial Intelligence and Deep Learning

Department: Engineering

ENGI4607: Artificial Intelligence and Deep Learning

Type Tied Level 4 Credits 10 Availability Available in 2023/24 Module Cap None. Location Durham
Tied to H100
Tied to H106
Tied to H108
Tied to H411
Tied to H412
Tied to H413
Tied to H911
Tied to H912
Tied to H913
Tied to H511
Tied to H512
Tied to H513
Tied to H711
Tied to H712
Tied to H713
Tied to H311
Tied to H312
Tied to H313
Tied to H811
Tied to H812
Tied to H813


  • MATH1551 Maths for Engineers and Scientists


  • None

Excluded Combination of Modules

  • 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

Learning Outcomes

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).
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.

Modes of Teaching, Learning and Assessment and how these contribute to the learning outcomes of the module

  • The module content is delivered in lectures and is reinforced by problem sheets, equipping students with the required problem-solving capability.
  • Students are able to make use of staff 'Tutorial 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 per lecture course.
  • 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.

Teaching Methods and Learning Hours

Activity Number Frequency Duration Total/Hours
Lectures 20 Typically 1 per week 1 Hour 20
Tutorial Hours As required Weekly sign-up sessions Up to 1 Hour 10
Formative exercises and self-study 70

Summative Assessment

Component: Coursework Component Weighting: 100%
Element Length / duration Element Weighting Resit Opportunity
Coursework 100%

Formative Assessment:

Attendance at all activities marked with this symbol will be monitored. Students who fail to attend these activities, or to complete the summative or formative assessment specified above, will be subject to the procedures defined in the University's General Regulation V, and may be required to leave the University