Introduction to Machine learning

Contributors

Anup Kumar

Questions

• What is machine learning?

• Why is it useful?

• What are its different approaches?

Objectives

• Provide the basics of machine learning and its variants.

• Learn how to do classification using the training and test data.

• Learn how to use Galaxy’s machine learning tools.

Contents

• What is machine learning?
• Types of machine learning
• Techniques for
  • Hyperparameter optimisation
  • Learning and evaluation of models
• Various applications of machine learning

Machine learning

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• Learns patterns from data
• Comprises of different fields
  • Linear algebra, statistics and probability
  • Programming
  • Data analysis
  • Visualization
• Applicable to data from multiple fields - protein and DNA sequences, weather data, stock and house prices, images …

]

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Variants of ML

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Classification

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• Supervised learning
• Learn/predict classes or targets
• Find decision boundary
• Linear and non-linear boundaries
• Algorithms are classifiers
• Examples
  • Tumor or no tumor
  • Rain or no rain
  • … ]

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Classification dataset

• Breast tumor dataset - Features and target

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Regression

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• Supervised learning
• Targets are real numbers
• Find fitting curve
• Linear or non-linear curves
• Algorithms are regressors
• Examples:
  • Temperature forecast
  • Stock/house prices
  • …

]

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Regression dataset

• Body fat dataset - features and target

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Hyperparameter optimisation

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• Grid search
• Random search

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Learning and evaluation

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• K-fold cross-validation
• Dataset in K equal parts
• Part == fold
• Learn on training set
• Evaluate on validation set

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Learning and evaluation

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• Training and test sets
• Learn on training set
• Evaluate on test set ]

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Applications of machine learning

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• BioInformatics
  • Protein structure prediction
  • Drug response prediction
  • Biological age prediction
  • Biomedical image analysis
  • …
• Computer vision/image recognition
• Natural language processing
• Speech recognition
• …

]

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References

• Machine learning for everyone - hhttps://vas3k.com/blog/machine_learning/
• Breast cancer dataset - https://archive.ics.uci.edu/dataset/15/breast+cancer+wisconsin+original
• Body fat dataset - https://rstudio-pubs-static.s3.amazonaws.com/65314_c0d1e5696cdd4e93a3784ea67f9e3d34.html

For additional references, please see tutorial’s References section

• Galaxy Training Materials (training.galaxyproject.org)

Speaker Notes

• If you would like to learn more about Galaxy, there are a large number of tutorials available.
• These tutorials cover a wide range of scientific domains.

Getting Help

• Help Forum (help.galaxyproject.org)

  

• Gitter Chat

  • Main Chat
  • Galaxy Training Chat
  • Many more channels (scientific domains, developers, admins)

Speaker Notes

• If you get stuck, there are ways to get help.
• You can ask your questions on the help forum.
• Or you can chat with the community on Gitter.

Join an event

• Many Galaxy events across the globe
• Event Horizon: galaxyproject.org/events

Speaker Notes

• There are frequent Galaxy events all around the world.
• You can find upcoming events on the Galaxy Event Horizon.

Key Points

• Machine learning algorithms learn features from data.
• It is used for multiple tasks such as classification, regression, clustering and so on.
• Multiple learning tasks can be performed using Galaxy's machine learning tools.
• For the classification and regression tasks, data is divided into training and test sets.
• Each sample/record in the training data has a category/class/label.
• A machine learning algorithm learns features from the training data and do predictions on the test data.

Thank you!