MEIN40370 Life sciences Machine Learning (Aut)

Academic Year 2021/2022

Machine Learning (ML) and Deep Learning (DL) are key enablers in transforming the field of personalised medicine. This module focuses specifically on various ML and DL techniques commonly applied in life sciences. The objective of this module is to familiarise students with the fundamental concepts of data science, focusing on the utilisation of machine learning and deep learning algorithms for scientific applications in the field biology, genetics and drug discovery.

The module will cover the basic concepts of machine learning and deep learning, common toolkits for ML and DL in life sciences and the practical implementation of various learning algorithms using biomedical examples. Evaluation of the performance of these algorithms will be performed together with the adoption of best practices to enable reproducibility and rigour of the developed solutions. The module will focus on a number of practical programming practices involving python machine learning and deep learning packages adopted by the industry.

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Curricular information is subject to change

Learning Outcomes:

Upon completion of the module, students should be able to:

1. Identify suitable machine learning / deep learning approaches given a specific task / application.
2. Design and implement data analytics pipeline using Python libraries using industry adopted platforms.
3. Critically evaluate the performance of machine learning / deep learning models.

Indicative Module Content:

a) Medical imaging analysis (Deep Learning for computer vision with various image types)

b) Multi-Omics - e.g., gene expression inference, analysis of human splicing codes - determination of disease, prediction of non-coding variant, etc.

c) Protein structure prediction - e.g., prediction of protein secondary structure, prediction of protein contact map, etc.

At the end of the module students will be able to implement the various techniques of ML/DL on the specific application themes such as those mentioned above. Students will explore the latest state-of-the-art models running on commercial platforms (e.g., TensorFlow on google).

Student Effort Hours: 
Student Effort Type Hours
Specified Learning Activities

50

Autonomous Student Learning

50

Lectures

24

Total

124

Approaches to Teaching and Learning:
Lectures
Continuous Assessment;
Enquiry & problem-based learning 
Requirements, Exclusions and Recommendations

Not applicable to this module.


Module Requisites and Incompatibles
Not applicable to this module.
 
Assessment Strategy  
Description Timing Open Book Exam Component Scale Must Pass Component % of Final Grade
Project: Two components (Project 1 - 50% Wk 1 to 6, and Project 2 - 50% Wk 7 to 12), where students will apply ML or DL modelling on two life science problems. Throughout the Trimester n/a Standard conversion grade scale 40% Yes

100


Carry forward of passed components
Yes
 
Resit In Terminal Exam
Spring No
Please see Student Jargon Buster for more information about remediation types and timing. 
Feedback Strategy/Strategies

• Feedback individually to students, on an activity or draft prior to summative assessment
• Feedback individually to students, post-assessment
• Group/class feedback, post-assessment

How will my Feedback be Delivered?

Not yet recorded.

Timetabling information is displayed only for guidance purposes, relates to the current Academic Year only and is subject to change.
 

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