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Throughout the 2021-22 academic year, we will be prioritising face to face teaching as part of a blended learning approach that builds on the lessons learned over the course of the Coronavirus pandemic. Teaching will vary between online and on-campus delivery through the year, and you should read guidance from the academic department for details of how this will work for a particular module. You can find out more about the University’s overall response to Coronavirus at: https://warwick.ac.uk/coronavirus.

ES3H3-15 Intelligent System Design

Department
School of Engineering
Level
Undergraduate Level 3
Module leader
Thomas Popham
Credit value
15
Module duration
15 weeks
Assessment
100% coursework
Study location
University of Warwick main campus, Coventry
Introductory description

ES3H3-15 Intelligent System Design

Module web page

Module aims

By the end of the module the student should be able to:

  1. Describe the typical software and hardware architectures of intelligent systems in various domains
  2. Apply machine learning techniques to solve real-world problems
  3. Apply computer vision techniques for solving problems such as face recognition and motion estimation.
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.

Computer Vision Topics:

  • Edges, corners, gradients
  • Feature detectors
  • Motion estimation / Tracking
  • Camera model / Stereo
  • Object detection
    Machine Learning Topics:
  • Linear/Ridge/Lasso Regression
  • Model fitting techniques: gradient descent, Newton's method.
  • Classification: Logistic Regression, Naive Bayes, GDA
  • Neural Networks: Back-propagation, shallow and deep architectures
Learning outcomes

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

  • 1. Describe the typical software and hardware architectures of intelligent systems in various domains
  • 2. Select, apply and evaluate machine learning techniques for solving real-world problems
  • 3. Select, apply and evaluate computer vision techniques for solving problems such as face recognition and motion estimation
Indicative reading list

Lei, B., Xu, G., Feng, M., van der Heijden, F., Zou, Y., de Ridder, D. and Tax, D.M., 2017.
“Classification, parameter estimation and state estimation: an engineering approach using
MATLAB”. John Wiley & Sons.

  • Murphy, Kevin P. “Machine learning: a probabilistic perspective”. MIT press, 2012. · Gomaa, Hassan. “Real-Time Software Design for Embedded Systems”. Cambridge University Press, 2016.
Subject specific skills

Systems Engineering approach, Software Engineering, Programming.

Transferable skills

Project Management, Team work, Presentations.

Study time

Type Required Optional
Project supervision 2 sessions of 2 hours (3%)
Practical classes 13 sessions of 2 hours (17%)
Online learning (independent) (0%) 2 sessions of 2 hours
Private study 120 hours (80%)
Total 150 hours
Private study description

120 hours guided independent study

Costs

No further costs have been identified for this module.

You must pass all assessment components to pass the module.

Assessment group A2
Weighting Study time
Lab Assessments 70%

Programming assignments / in-class tests

Group Project 30%
Feedback on assessment
  • Support through advice and feedback hours.
  • Written feedback on individual projects
  • Written feedback on group projects
  • Cohort feedback in lectures on coursework performance
    Each of the component must be passed (>=30%) in order to pass the module

Courses

This module is Core for:

  • Year 3 of UESA-HH35 BEng Systems Engineering
  • Year 4 of UESA-HH34 BEng Systems Engineering with Intercalated Year
  • Year 3 of UESA-HH31 MEng Systems Engineering

This module is Core optional for:

  • Year 3 of UESA-H115 MEng Engineering with Intercalated Year
  • Year 4 of UESA-HH32 MEng Systems Engineering with Intercalated Year

This module is Optional for:

  • Year 3 of UESA-H113 BEng Engineering
  • UESA-H114 MEng Engineering
    • Year 3 of H114 Engineering MEng
    • Year 3 of H114 Engineering MEng
  • Year 4 of UESA-H115 MEng Engineering with Intercalated Year

This module is Option list A for:

  • Year 4 of UESA-H111 BEng Engineering with Intercalated Year
  • Year 4 of UESA-H118 BEng Engineering with Intercalated Year
  • Year 3 of UESA-H112 BSc Engineering