Course unit details:
Machine Learning and Advanced Data Methods
Unit code | IIDS67682 |
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Credit rating | 15 |
Unit level | FHEQ level 7 – master's degree or fourth year of an integrated master's degree |
Teaching period(s) | Semester 2 |
Available as a free choice unit? | No |
Overview
Biomedical and health informatics is founded on the usage and application of computational algorithms to high-throughput biological data and patient-level information. With the growing availability of big data in the biomedical domain, machine learning and other advanced approaches are becoming essential. Informaticians need to have a good understanding of these different algorithms, methodologies and analysis pipelines, and their applicability to different data types and research questions.
Aims
This unit aims to:
- Introduce different types of biomedical and health data, including from high-throughput biological experiments and patient-level information resources
- Examine different approaches and pipelines for data analysis
- Learn the principles underlying popular machine learning algorithms
- Gain experience in applying machine learning to real-world datasets
- Critically appraise analysis pipelines and data usage and be able to make suggestions on improvements
Learning outcomes
- Introduce different types of biomedical and health data, including from high-throughput biological experiments and patient-level information resources
- Examine different approaches and pipelines for data analysis
- Learn the principles underlying popular machine learning algorithms
- Gain experience in applying machine learning to real-world datasets
- Critically appraise analysis pipelines and data usage and be able to make suggestions on improvements
Teaching and learning methods
This unit will be delivered in a face-to-face format over six days: lectures and open discussions will provide basic and core knowledge and introduce concrete examples and encourage attendees to draw upon their own reading and experience. Practical sessions will provide hands-on experience, with real-world application of the theoretical material. Coding will be carried out using python via web-based jupyter notebooks that will be available to work on throughout the course. All materials will be introduced on Blackboard.
Knowledge and understanding
- Demonstrate a critical understanding of advanced machine learning methods and techniques
- Apply these methods to a range of problems
- Evaluate different scenarios in which such methods would be applicable
- Assess the possible ethical implications of Machine Learning methods when applied to biomedical data sets
Intellectual skills
- Critically evaluate the strengths and limitations of different data analysis methods and approaches
- Apply analytical methods to a range of datasets and research questions
- Demonstrate an understanding and draw conclusions from the results of analytical methods
Practical skills
- Produce and execute a data analysis pipeline using python
- Carry out exploratory data analysis using visualisation and clustering methods
- Apply and evaluate the performance of different machine learning algorithms
Transferable skills and personal qualities
- Improved coding skills and experience with Jupyter notebooks
- Be able to apply data analysis skills to a broad range of problems
- Use appropriate software and tools for data analysis in biomedical research
Assessment methods
3 x interactive online assessments
Three online assessments carried out through interactive notebooks
The three assessments will cover:
1. Exploratory data analysis
2. Supervised machine learning
3. Advanced topics (neural networks and ethics)
Recommended reading
- Elements of Statistical Learning (Hastie, Tibshirani & Friedman, Springer, 2009, second edition)
- Machine Learning in Healthcare Informatics (Dua et al., Springer, 2014)
- Machine Learning and AI for Healthcare: Big Data for Improved Health Outcomes (Panesar, Apress, 2019)
Study hours
Scheduled activity hours | |
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Practical classes & workshops | 25 |
Tutorials | 25 |
Independent study hours | |
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Independent study | 100 |
Teaching staff
Staff member | Role |
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David Wong | Unit coordinator |
Magnus Rattray | Unit coordinator |