Introductory description

The module introduces key concepts related to Machine Learning (ML) and Artificial Intelligence (AI). Through exploring Linear and Logistic Regression techniques, students will embark on learning critical skills to tackle more advanced Neural Networks architecture. Key focus will be made on data management workflow to enhance performance and robustness of ML algorithms. Relevant AI techniques that are widely used in development of automated vehicles and related areas will be explored including: Supervised and Unsupervised Learning, Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks and Reinforcement Learning.

Module aims

Outline syllabus

This is an indicative module outline only to give an indication of the sort of topics that may be covered. Actual sessions held may differ.

• A general overview of AI systems and their applications
• Data science basis for machine intelligence:
• Understanding experimental data and fitting
• Clustering and classification
• Deep learning systems
• Introduction to neural networks
• Convolutional neural networks
• Recurrent neural networks
• Reinforced learning
• Tutorials on tools and examples

Learning outcomes

By the end of the module, students should be able to:

• Critique advanced AI architectures in a given operational design domain [AHEP:4, M1, M2, M4, M5, M7]
• Implement Machine Learning algorithms by mastering relevant tools [AHEP:4, M1, M2, M3, M4]
• Choose, develop and evaluate critically neural networks [AHEP:4, M1, M2, M3, M4, M5, M7]
• Critically analyse data sets and techniques to train and test machine learning algorithms [AHEP:4, M1]
• Demonstrate a critical understanding of Machine Learning algorithms and their architecture [AHEP:4, M1, M2, M3]

Indicative reading list

• GOODFELLOW, Ian; BENGIO, Yoshua; COURVILLE, Aaron. Deep learning (adaptive
  computation and machine learning series). Adaptive Computation and Machine Learning
  series, 2016, 800.
• RUSSELL, Stuart Jonathan, et al. Artificial intelligence: a modern approach. Upper Saddle
  River: Prentice hall, 2003.
• URMSON, Chris, et al. Tartan racing: A multi-modal approach to the darpa urban
  challenge. 2007.
• GUTTAG, John V. Introduction to computation and programming using Python. Mit Press,

• SAMARASINGHE, Sandhya. Neural networks for applied sciences and engineering: from
  fundamentals to complex pattern recognition. CRC Press, 2016.
• ASIMOV, Isaac. I, Robot, Robot series. 1950.

A variety of up-to-date sources including:

• Latest government / UK Automotive Council roadmaps for autonomous vehicles
• Latest automotive legislation and standards
• Current academic research in the field of smart connected autonomous vehicles

View reading list on Talis Aspire

Subject specific skills

Basic knowledge of AI techniques that are widely used in development of automated vehicles and related areas, Deep Learning techniques that are heavily used, including: Supervised and Unsupervised Learning, Artificial Neural Networks, Convolutional Neural Networks, Recurrent Neural Networks.

Transferable skills

Critical Thinking, Problem solving, Communication, Information literacy (research skills), Digital literacy, Professionalism