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Study

Machine Learning For Biomedical Applications

Machine learning

Module code: 6CCYB064

Module credits: 15

Module convenors: Dr Maria Deprez, Dr Emma Robinson                              

Aims

The module will provide the students with a fundamental grounding in the theoretical and computational skills required to apply machine learning tools to real-world problems. It will provide an understanding of the application of these skills to explore complex high-dimensional data sets; providing an overview of active research areas in machine learning, with biomedical applications.

Learning outcomes

On completion of the course the students should be able to:

  • Have acquired a basic understanding of the most fundamental concepts related to machine learning
  • Understand and be able to apply a range of commonly used machine learning techniques
  • Have acquired practical computational skills that are needed to manipulate complex data sets
  • Know how to practically design machine learning experiments
  • Apply techniques learnt to domains related to biomedical applications

Teaching Methods

There will be 40 contact hours. This module emphasises hands-on tutorial sessions which will be a mix of lecture-style delivery followed immediately by problem solving activities.

Syllabus

  • Introduction to Machine Learning for Biomedical data analysis.
  • Introduction to programming in Python and Python packages: NumPy, Matplotlib, Pandas, scikit-learn.
  • Fundamentals of Machine Learning: regression, classification and clustering; overfitting and cross-validation; performance measures.
  • Linear, penalised, logistic and kernel regression, Gaussian process.
  • Support vector machines, the kernel trick.
  • Dimensionality reduction: Principal Component Analysis, Independent Component Analysis, Manifold learning
  • Ensemble methods: Bagging, Boosting, Random Forest
  • Feature selection
  • Unsupervised methods: Gaussian Mixture Model, Markov Random Fields, Graph-cut algorithm
  • Introduction to Deep Learning with TensorFlow: Multi-layer perceptron, Deep Convolutional Neural Networks.

Summative Assessment 

Details of the module's summative assessment/s 

Type

Weighting

Written exam (2 hours) (January)

50%

2 coursework submissions

50%

 

Formative Assessment 

Formative assessment is done informally during lab sessions.

 

e-Learning: 6CCYB064 on KEATS

 

Suggested Reading

Pattern Recognition and Machine Learning, Christopher M. Bishop

Hands-On Machine Learning with Scikit-Learn and TensorFlow, Aurelien Geron

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