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Module Catalogue

WM919-15 Machine Intelligence and Data Science

Department
WMG
Level
Taught Postgraduate Level
Module leader
Karim El Haloui
Credit value
15
Module duration
2 weeks
Assessment
100% coursework
Study location
University of Warwick main campus, Coventry

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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 architectures. 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.

Module aims

The aim is to equip students with a solid knowledge of key AI techniques pervasive to the development of advanced driving systems 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.

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
  • Classification
  • Deep learning systems
  • Introduction to neural networks
  • Convolutional neural networks
  • Recurrent neural networks
  • 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

Study time

Type Required
Lectures 20 sessions of 1 hour (13%)
Seminars 1 session of 1 hour (1%)
Tutorials 9 sessions of 1 hour (6%)
Private study 60 hours (40%)
Assessment 60 hours (40%)
Total 150 hours

Private study description

In-depth reading around the subject

Costs

No further costs have been identified for this module.

You must pass all assessment components to pass the module.

Assessment group A4
Weighting Study time Eligible for self-certification
Assessed work as specified by department 80% 42 hours Yes (extension)

A collection of 3 to 4 problems depending on their length and complexity to be solved by students.
In-module assessment 20% 18 hours No

Based on self-study hours on basic concepts of Machine Learning and neural networks.
Feedback on assessment

Individual written feedback. Formative assessment during tutorials and class quizzes.

There is currently no information about the courses for which this module is core or optional.