Introduction to Machine learning

Anup Kumar

Updated: PURL: gxy.io/GTN:S00136

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What is machine learning?
Why is it useful?
What are its different approaches?

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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.

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

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Machine learning

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 ...

Machine learning

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Variants of ML

ML variants

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Classification

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
- ...

Classification

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Classification dataset

Breast tumor dataset - Features and target

Classification dataset

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Regression

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

Regression

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Regression dataset

Body fat dataset - features and target

Ensemble model

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Hyperparameter optimisation

Grid search
Random search

Hyperparameter optimisation

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Learning and evaluation

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

Learning and evaluation

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Learning and evaluation

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

Learning and evaluation

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Applications of machine learning

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

Learning and evaluation

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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

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For additional references, please see tutorial's References section

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Galaxy Training Materials (training.galaxyproject.org)

Screenshot of the gtn stats page with 21 topics, 170 tutorials, 159 contributors, 16 scientific topics, and a growing community

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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)

Galaxy Help

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

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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

Event schedule

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

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.

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Thank You!

This material is the result of a collaborative work. Thanks to the Galaxy Training Network and all the contributors!

Author(s)	Anup Kumar
Reviewers

Tutorial Content is licensed under Creative Commons Attribution 4.0 International License.

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Introduction to Machine learning

question Questions

objectives Objectives

Contents

Machine learning

Variants of ML

Classification

Classification dataset

Regression

Regression dataset

Hyperparameter optimisation

Learning and evaluation

Learning and evaluation

Applications of machine learning

References

For additional references, please see tutorial's References section

Getting Help

Join an event

keypoints Key points

Thank You!

question Questions

Help