Introductory description

ES3H3-15 Intelligent System Design

Module web page

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.

Computer Vision Topics:

• Edges, corners, gradients
• Feature detectors
• Image Filtering and Convolution
• Motion estimation / Tracking
• 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. Perform end-to-end development of a machine learning system, including: gathering of datasets; model selection and training; and testing of system performance [C6, M6]
• 2. Select, apply and evaluate machine learning techniques for solving real-world problems [C1, M1, C2, M2, C3, M3, C6, M6, C12, M12]
• 3. Select, apply and evaluate computer vision techniques for solving problems such as face recognition and motion estimation [C1, M1, C2, M2, C3, M3]

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

Ability to conceive, make and realise a component, product, system or process. Ability to be pragmatic, taking a systematic approach and the logical and practical steps necessary for, often complex, concepts to become reality. Ability to seek to achieve sustainable solutions to problems and have strategies for being creative and innovative.

Transferable skills

Numeracy: apply mathematical and computational methods to communicate parameters, model and optimize solutions.

Apply problem solving skills, information retrieval, and the effective use of general IT facilities.

Communicate (written and oral; to technical and non-technical audiences) and work with others.

Exercise initiative and personal responsibility, including time management, which may be as a team member or leader

Awareness of the nature of engineering business and enterprise in the creation of economic and social value

Overcome difficulties by employing skills, knowledge and understanding in a flexible manner

Be professional in their outlook, be capable of team working, be effective communicators, and be able to exercise responsibility and sound management approaches.