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Accessibility statement
Computers are getting smarter. Intelligent assistants like Alexa and Siri, image searches that find the topic of a photo, and self-driving cars – these intelligent systems use machine learning to develop their expertise. In this module, you’ll learn about various machine learning techniques but concentrate on deep neural learning. You’ll learn about the underlying theory and get hands-on experience creating, training, evaluating, and using machine learning systems. You’ll also examine how these technologies are used and misused and what that means for our societies and communities.
The module is divided into several blocks. Each covers a different aspect of machine learning.
You’ll start with an introduction that outlines some of the issues surrounding machine learning, including questions about how machine learning systems are used and their social effects.
Deep neural learning is introduced, and neural networks are examined. You’ll create, train, and evaluate some neural networks to perform tasks such as handwriting recognition. You’ll also see how these networks don’t scale up to larger problems.
Convolutional neural networks solve many of these problems. You’ll learn how these networks start by identifying small features in their inputs (typically images). Successive layers in the inputs combine these features into larger ones, eventually leading to a classification in the image.
Recurrent neural networks operate on time-dependent data, such as language. You’ll learn how they retain information seen earlier to interpret what’s happening now. You’ll use them to understand and generate some text.
Autoencoders teach themselves to compress and reconstruct their inputs. You’ll see how to use this for both compression and to clean data and replace missing data. You’ll see how this can generate “deepfakes” to fool people, and what that means for trust.
You’ll also look at alternatives to deep learning and compare their strengths and weaknesses to the deep learning systems you’ve seen. Part of the difference lies in how the data should be prepared for different systems. As well as the time needed, preparation is another way for bias to creep into the system.
The conclusion asks you to review the technical aspects of machine learning, consider how these systems are used, and the effects this could have on individuals and society.
To begin TM358, you must have passed the following module:
Key topics to revise include:
You’ll get help and support from an assigned tutor throughout your module.
They’ll help by:
Online tutorials run throughout the module. While they’re not compulsory, we strongly encourage you to participate. Where possible, we’ll make recordings available.
Course work includes:
You’ll have access to a module website, which includes:
The OU strives to make all aspects of study accessible to everyone, and this Accessibility Statement outlines what studying TM358 involves. You should use this information to inform your study preparations and any discussions with us about how we can meet your needs.
To find out more about what kind of support and adjustments might be available, contact us or visit our Disability support website.
Machine learning and artificial intelligence (TM358) starts once a year – in October.
It will next start in October 2026.
We expect it to start for the last time in October 2029.
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