Introductory description

The aim is to equip students with a solid knowledge of key AI techniques that are widely used in development of automated vehicles and related areas. The module will focus on practical aspects of AI where the students will gain a strong high level understanding of the underlying theory. The emphasis will be on Machine Learning and Deep Learning techniques that are at the nexus of the development of future technologies such as automated driving.

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
• Industry expert seminars
• Tutorials on tools and examples

Learning outcomes

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

• 1. Demonstrate systematic high-level knowledge of AI systems
• 2. Demonstrate the mastery of relevant tools used to implement machine intelligence algorithms
• 3. Choose, implement and evaluate critically neural networks
• 4. Critically analyse data sets and techniques to train and test machine learning algorithms

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

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