Introduction to Machine learning

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# Introduction to Machine learning

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				<a href="/training-material/hall-of-fame/anuprulez/" class="contributor-badge contributor-anuprulez"><img src="https://avatars.githubusercontent.com/anuprulez?s=36" alt="Anup Kumar avatar" width="36" class="avatar" />
    Anup Kumar</a></li>
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### <i class="far fa-question-circle" aria-hidden="true"></i><span class="visually-hidden">question</span> Questions

- What is machine learning?

- Why is it useful?

- What are its different approaches?

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### <i class="fas fa-bullseye" aria-hidden="true"></i><span class="visually-hidden">objectives</span> 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.

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# Contents

- 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

.pull-left[
- 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 ...

]

.pull-right[ ![Machine learning](/training-material/topics/statistics/tutorials/machinelearning/images/it1.png)]

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# Variants of ML

.center[ ![ML variants](/training-material/topics/statistics/tutorials/machinelearning/images/it2.png)]

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# Classification

.pull-left[
- 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
    - ...
]

.pull-right[ ![Classification](/training-material/topics/statistics/tutorials/machinelearning/images/it3.png)]

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# Classification dataset

- Breast tumor dataset - Features and target

.center[  ![Classification dataset](/training-material/topics/statistics/tutorials/machinelearning/images/it4.png)]

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# Regression

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

]

.pull-right[ ![Regression](/training-material/topics/statistics/tutorials/machinelearning/images/it5.png)]

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# Regression dataset

- Body fat dataset - features and target

.center[ ![Ensemble model](/training-material/topics/statistics/tutorials/machinelearning/images/it6.png)]

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# Hyperparameter optimisation

.pull-left[
- Grid search
- Random search

]

.pull-right[ ![Hyperparameter optimisation](/training-material/topics/statistics/tutorials/machinelearning/images/it7.png)]

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# Learning and evaluation

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

]

.pull-right[ ![Learning and evaluation](/training-material/topics/statistics/tutorials/machinelearning/images/it8.png)]

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# Learning and evaluation

.pull-left[
- Training and test sets
- Learn on training set
- Evaluate on test set
]

.pull-right[ ![Learning and evaluation](/training-material/topics/statistics/tutorials/machinelearning/images/it9.png)]

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# Applications of machine learning

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

]

.pull-right[ ![Learning and evaluation](/training-material/topics/statistics/tutorials/machinelearning/images/it10.png)]

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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](https://training.galaxyproject.org))

![Screenshot of the gtn stats page with 21 topics, 170 tutorials, 159 contributors, 16 scientific topics, and a growing community](/training-material/topics/introduction/images/gtn_stats.png)

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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.

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# Getting Help

- **Help Forum** ([help.galaxyproject.org](https://help.galaxyproject.org))

![Galaxy Help](/training-material/topics/introduction/images/galaxy_help.png)

- **Gitter Chat**
    - [Main Chat](https://gitter.im/galaxyproject/Lobby)
    - [Galaxy Training Chat](https://gitter.im/Galaxy-Training-Network/Lobby)
    - Many more channels (scientific domains, developers, admins)

???

- 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.

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# Join an event

- Many Galaxy events across the globe
- Event Horizon: [galaxyproject.org/events](https://galaxyproject.org/events)

![Event schedule](/training-material/topics/introduction/images/event_horizon.png)

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

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### <i class="fas fa-key" aria-hidden="true"></i><span class="visually-hidden">keypoints</span> 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.

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## Thank You!

This material is the result of a collaborative work. Thanks to the [Galaxy Training Network](https://training.galaxyproject.org) and all the contributors!

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		<td><abbr title="These people wrote the bulk of the tutorial, they may have done the analysis, built the workflow, and wrote the text themselves.">Author(s)</abbr></td>
		<td>
			<a href="/training-material/hall-of-fame/anuprulez/" class="contributor-badge contributor-anuprulez"><img src="https://avatars.githubusercontent.com/anuprulez?s=36" alt="Anup Kumar avatar" width="36" class="avatar" />
    Anup Kumar</a>
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	</tr>

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		<td><abbr title="These people reviewed this material for accuracy and correctness">Reviewers</abbr></td>
		<td>
			<a href="/training-material/hall-of-fame/bgruening/" class="contributor-badge contributor-badge-small contributor-bgruening"><img src="https://avatars.githubusercontent.com/bgruening?s=36" alt="Björn Grüning avatar" width="36" class="avatar" /></a><a href="/training-material/hall-of-fame/teresa-m/" class="contributor-badge contributor-badge-small contributor-teresa-m"><img src="https://avatars.githubusercontent.com/teresa-m?s=36" alt="Teresa Müller avatar" width="36" class="avatar" /></a><a href="/training-material/hall-of-fame/martenson/" class="contributor-badge contributor-badge-small contributor-martenson"><img src="https://avatars.githubusercontent.com/martenson?s=36" alt="Martin Čech avatar" width="36" class="avatar" /></a><a href="/training-material/hall-of-fame/dadrasarmin/" class="contributor-badge contributor-badge-small contributor-dadrasarmin"><img src="https://avatars.githubusercontent.com/dadrasarmin?s=36" alt="Armin Dadras avatar" width="36" class="avatar" /></a></td>
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Tutorial Content is licensed under <a rel="license" href="http://creativecommons.org/licenses/by/4.0/">Creative Commons Attribution 4.0 International License</a>.<br/>