BIMAI2: AI and Big Data in Research and Healthcare

Module code: BIMAI2

Module provider: School of Biological Sciences

Credits: 20

Level: Postgraduate Masters

When you'll be taught: Semester 2

Module convenor: Professor William Holderbaum, email: w.holderbaum@reading.ac.uk

Pre-requisite module(s):

Co-requisite module(s):

Pre-requisite or Co-requisite module(s):

Module(s) excluded:

Placement information: NA

Academic year: 2024/5

Available to visiting students: No

Talis reading list: No

Last updated: 23 May 2024

Overview

Module aims and purpose

This module will provide students with a comprehensive understanding of the emerging field of Artificial Intelligence (AI), machine learning and Big Data analytics in the context of biomedical research and healthcare applications. This module explores the potential of AI and Big Data techniques in drug discovery, diagnostics, healthcare analytics and clinical decision-making. Throughout the module, students will engage in practical exercises and hands-on projects to reinforce their understanding of key concepts and will explore case studies from industry. Students will have the opportunity to work with real-world datasets, develop predictive models, and evaluate the performance of AI algorithms. 

Module learning outcomes

By the end of the module, it is expected that students will be able to: 

1. Explain the key principles underpinning AI and machine learning.  
2. Critically evaluate how the different types of data used in biotechnology and healthcare, such as genomics, proteomics, electronic health records, and medical imaging, can be used in AI and machine learning applications. 
3. Apply AI techniques in the context of biotechnology research and healthcare. 
4. Critically analyse the challenges and opportunities associated with implementing AI and machine learning in biotechnology and healthcare settings. 

Module content

• Overview of AI and machine learning technology, including supervised and unsupervised machine learning, neural networks and deep learning. 
• Introduction to the use of big data in healthcare, including the different types of data that are available. 
• AI in diagnostics. 
• AI and machine learning in the omics era. 

Structure

Teaching and learning methods

Teaching will be delivered through formal lectures and practical workshops in which students will discuss key concepts in AI and big data, with the aid of case studies provided by industry. Students will gain hands-on experience of using AI techniques in practical workshops and will also be expected to learn through self-directed study and through group working. Learning will be assessed by a written report in which students will evaluate a provided dataset and assess the most appropriate machine learning tools to use in analysing it. 

Study hours

At least 40 hours of scheduled teaching and learning activities will be delivered in person, with the remaining hours for scheduled and self-scheduled teaching and learning activities delivered either in person or online. You will receive further details about how these hours will be delivered before the start of the module.

Please note the independent study hours above are notional numbers of hours; each student will approach studying in different ways. We would advise you to reflect on your learning and the number of hours you are allocating to these tasks.

Semester 1 The hours in this column may include hours during the Christmas holiday period.

Semester 2 The hours in this column may include hours during the Easter holiday period.

Summer The hours in this column will take place during the summer holidays and may be at the start and/or end of the module.

Assessment

Requirements for a pass

Students need to achieve an overall module mark of 50% to pass this module.

Summative assessment

Penalties for late submission of summative assessment

The Support Centres will apply the following penalties for work submitted late:

Assessments with numerical marks

• where the piece of work is submitted after the original deadline (or any formally agreed extension to the deadline): 10% of the total marks available for that piece of work will be deducted from the mark for each working day (or part thereof) following the deadline up to a total of three working days;
• the mark awarded due to the imposition of the penalty shall not fall below the threshold pass mark, namely 40% in the case of modules at Levels 4-6 (i.e. undergraduate modules for Parts 1-3) and 50% in the case of Level 7 modules offered as part of an Integrated Masters or taught postgraduate degree programme;
• where the piece of work is awarded a mark below the threshold pass mark prior to any penalty being imposed, and is submitted up to three working days after the original deadline (or any formally agreed extension to the deadline), no penalty shall be imposed;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension to the deadline): a mark of zero will be recorded.

Assessments marked Pass/Fail

• where the piece of work is submitted within three working days of the deadline (or any formally agreed extension of the deadline): no penalty will be applied;
• where the piece of work is submitted more than three working days after the original deadline (or any formally agreed extension of the deadline): a grade of Fail will be awarded.

The University policy statement on penalties for late submission can be found at: /cqsd/-/media/project/functions/cqsd/documents/qap/penaltiesforlatesubmission.pdf

You are strongly advised to ensure that coursework is submitted by the relevant deadline. You should note that it is advisable to submit work in an unfinished state rather than to fail to submit any work.

Formative assessment

Formative assessment is any task or activity which creates feedback (or feedforward) for you about your learning, but which does not contribute towards your overall module mark.

Seminars will provide students with an opportunity to gain feedback on their understanding of the topics covered in the module. 

Reassessment

Additional costs