1 What this Module is About
1.1 Introduction from the Module Leader
This module introduces students to the various state-of-the-art intelligent systems techniques, machine learning, algorithms and methodologies. The module strikes a good balance between theory and practice and rather emphasise the latter whenever possible. Diverse topics will be covered to provide a wide understanding and intuition into which technique is more applicable at which scenario. The module demonstrates several intelligent systems use-cases in the context of modern AI applications to help solve practical problems and scenarios such as smart cities, health, lifestyle, businesses, manufacturing etc. Easy to use tools will be utilized such as RapidMiner.
The module touches upon the main areas of Data Science and Computational Intelligence; namely supervised and unsupervised machine learning techniques. In particular, the module will focus on machine learning and its vast applicability to many areas of computing such as security, enterprise systems, customer segments, fraud detection, classification and regression. A special attention will be given to deep neural network as an emerging technique that encompasses several frameworks that are all on demand by theindustry.
The module involves two coursework assessments. Attending the lab sessions is an essential training for both the coursework since the second assignment has a strong practical and report writing element in it.
I hope you enjoy this module and find it interesting and useful for your soon coming career. Dr Gopal Singh Jamnal
1.2 Module Aims
To give students a critical awareness of the latest development in intelligent systems and the use of machine learning.
To enable students to critically appraise the actual and potential ways in which these developments could be applied in at least one of the following context: smart cities, health, business, manufacturing, etc…
1.3 Module Learning Outcomes
LO1: Critically appraise and evaluate basic principles and motivations behind the ideas of machine learning and intelligent techniques and algorithms drawing on current theory and practice.
LO2: Develop an appreciation of the role and machine learning models and their architecture based on research and academic literature.
LO3: Critically evaluate the machine learning models lifecycle and methodologies in terms of practical and theoretical research.
LO4: Build and evaluate a machine learning model that utilizes different techniques.
1.4 Module Learning Activities
Lectures will be used to introduce much of the material. There will be a range of lab and written exercises in the tutorials designed to: (i) reinforce the theory and develop a critical awareness of intelligent systems applications; (ii) use of intelligent systems and tools to solve given problem scenarios; (iii) develop knowledge representation and reasoning engine for a range of problems.
A range of additional resources will be made available to the students via MyBeckett e.g. further exercises, readings and videos.
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