Module Details

Module Code: COMP9085
Title: Machine Learning in Biology
Long Title: Applied Machine Learning in Biology
NFQ Level: Expert
Valid From: Semester 1 - 2021/22 ( September 2021 )
Duration: 1 Semester
Credits: 5
Field of Study: 4811 - Computer Science
Module Delivered in: 3 programme(s)
Module Description: The biological sciences field is becoming increasingly data-rich and information-intensive.
Machine Learning techniques promise to be useful tools for better analysing the growing amount of available biological data, in order to select and extract needed knowledge. This module will focus on the application of machine learning to real-world biological data analysis problems. It will also equip students with the skills to comprehensively evaluate models and apply appropriate pre-processing methods.
 
Learning Outcomes
On successful completion of this module the learner will be able to:
# Learning Outcome Description
LO1 Analyse machine learning workflows to facilitate pre-processing, dimensionality reduction and model selection.
LO2 Compare and apply appropriate machine learning algorithms to specific Biological datasets.
LO3 Develop supervised machine learning models for classification tasks.
LO4 Develop unsupervised machine learning models for clustering tasks.
LO5 Evaluate the accuracy of predictive models using standard methods and interpret their results.
Dependencies
Module Recommendations

This is prior learning (or a practical skill) that is strongly recommended before enrolment in this module. You may enrol in this module if you have not acquired the recommended learning but you will have considerable difficulty in passing (i.e. achieving the learning outcomes of) the module. While the prior learning is expressed as named MTU module(s) it also allows for learning (in another module or modules) which is equivalent to the learning specified in the named module(s).
Incompatible Modules
These are modules which have learning outcomes that are too similar to the learning outcomes of this module. You may not earn additional credit for the same learning and therefore you may not enrol in this module if you have successfully completed any modules in the incompatible list.
No incompatible modules listed
Co-requisite Modules
No Co-requisite modules listed
Requirements

This is prior learning (or a practical skill) that is mandatory before enrolment in this module is allowed. You may not enrol on this module if you have not acquired the learning specified in this section.
No requirements listed
 
Indicative Content
Machine Learning Fundamentals
Needs, definitions, challenges and what to expect from ML, learning types and overview of popular algorithms etc.
Feature engineering and pre-processing
Feature engineering on text, numeric, temporal and image data; Application of a standard machine learning pre-processing methodology using techniques such as dimensionality reduction, model selection, feature selection.
Model building, evaluation and hyper-parameter optimization
Overview of evaluation metrics such as precision, recall, confusion matrices.
Supervised Machine Learning
Building predictive models using Scikit-Learn for solving classification and regression problems. Using machine learning algorithms such as instance based learners, naive Bayes, ensembles etc.
Unsupervised Machine Learning
Unsupervised learning techniques such as association rule mining, K-Means, density-based and hierarchical clustering techniques.
Module Content & Assessment
Assessment Breakdown%
Coursework100.00%

Assessments

Coursework
Assessment Type Project % of Total Mark 50
Timing Week 6 Learning Outcomes 1,2,3,5
Assessment Description
Apply a range of machine learning classification algorithms to a complex real-world problem such as Biological Image Analysis. The findings should be documented.
Assessment Type Project % of Total Mark 50
Timing Week 13 Learning Outcomes 1,2,4,5
Assessment Description
Apply clustering algorithms for Biological Data. A comprehensive reporting evaluating the performance of the algorithms should be submitted.
No End of Module Formal Examination
Reassessment Requirement
Coursework Only
This module is reassessed solely on the basis of re-submitted coursework. There is no repeat written examination.

The University reserves the right to alter the nature and timings of assessment

 

Module Workload

Workload: Full Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivers the concepts and theories underpinning the learning outcomes. Every Week 2.00 2
Lab Contact Application of learning to case studies and project work. Every Week 2.00 2
Independent Learning Non Contact Student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
Workload: Part Time
Workload Type Contact Type Workload Description Frequency Average Weekly Learner Workload Hours
Lecture Contact Delivers the concepts and theories underpinning the learning outcomes. Every Week 2.00 2
Lab Contact Application of learning to case studies and project work. Every Week 2.00 2
Independent Learning Non Contact Student reads recommended papers and practices implementation. Every Week 3.00 3
Total Hours 7.00
Total Weekly Learner Workload 7.00
Total Weekly Contact Hours 4.00
 
Module Resources
Recommended Book Resources
  • Aurélien Géron. Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow, 2nd Edition. O'Reilly Media, Inc., [ISBN: 9781492032649].
Recommended Article/Paper Resources
  • Tabe-Bordbar, S., Emad, A., Zhao, S. D. & Sinha, S.. (2018), A closer look at cross-validation for assessing the accuracy of gene regulatory networks and models, Engineering, Sci. Rep. 8, 6620.
  • David T. Jones. (2019), Setting the standards for machine learning in biology, Nature Reviews Molecular Cell Biology.
Other Resources
 
Module Delivered in
Programme Code Programme Semester Delivery
CR_SCOBI_9 Master of Science in Computational Biology 3 Mandatory
CR_SNUHA_9 Master of Science in Nutrition & Health Analytics 3 Mandatory
CR_SCPBI_9 Postgraduate Diploma in Science in Computational Biology 3 Mandatory