Skip to main content Skip to navigation

Machine Intelligence and Data Science

Availability

This module is only available to students on the MSc in Smart, Connected, and Autonomous Vehicles and as part of the Part Time TAS JLR scheme.

Pre-requisites

n/a

Introduction

The aim is to help students build a solid knowledge of key AI techniques that are widely used in development of autonomous vehicles. To understand the application context, an overview of autonomous vehicles technology will be provided, including: Localization, Sensing & Perception and Motion Planning. The module will then focus on practical aspects of AI while the students will gain a strong high level understanding of the underlying theory. The emphasis will be on Deep Learning techniques that are heavily used in driverless vehicles, including: Convolutional Neural Networks (CNN), Supervised and Unsupervised Learning, Recurrent Neural Networks.

Objectives

Upon successful completion participants will be able to:

  • Demonstrate a complete understanding of the high level architectures and principles of autonomous driving systems
  • Demonstrate systematic high-level knowledge of the AI systems
  • Demonstrate a critical understanding of neural networks architecture
  • Demonstrate a comprehensive understanding of Deep Learning
  • Demonstrate the mastery of relevant tools used to achieve machine intelligence
  • Critically evaluate the socio economic implications of the use of AI technologies in automated transport
  • Practically analyse and optimise different types of neural networks used for different automotive tasks
  • Critically analyse data sets and techniques for clustering and classification

Syllabus

  • An overview of autonomous vehicles technology
    • System architecture
    • Localisation
    • Sensing and perception
    • Motion planning in complex environments
  • A general overview of AI systems
  • Data science basis for machine intelligence:
    • Understanding experimental data and fitting
    • Clustering and classification
  • Deep learning systems
    • Introduction to neural networks
    • Deep learning neural networks
    • Reinforced learning
    • Supervised and unsupervised learning
    • Convolutional neural networks
    • Recurrent neural networks
  • Industry expert seminars
  • Tutorials on tools and examples

Assessment

PMA: 70% of final mark

IMA: 30% of final mark

Duration

42 contact hours (to include lectures, tutorials, practicals/workshops, presentations, case studies and syndicate exercises)