Concepts in Machine Learning
====

URL to this page: https://hackmd.io/@k8hertweck/conceptsML

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This page is for easy access to links we'll use during class. You don't need to do anything with this information until directed by your instructor.

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Complete class notes [here](https://github.com/fredhutchio/concepts_machine_learning)

**Week 1: Intro and Conceptual Overview; Machine Learning and Experimental Design**

* Files
* [Week 1 Slides](https://github.com/fredhutchio/concepts_machine_learning/blob/master/slides/ML_concepts_wk1_slides.pdf)
* Reference
* [CRISP-DM](https://en.wikipedia.org/wiki/Cross-industry_standard_process_for_data_mining)
* [Machine Learning](https://en.wikipedia.org/wiki/Machine_learning)

**Week 2: Case Study in Regression**

* Files
* [Week 2 Slides](https://github.com/fredhutchio/concepts_machine_learning/blob/master/slides/ML_concepts_wk2_slides.pdf)
* Reference
* [Supervised Learning](https://en.wikipedia.org/wiki/Supervised_learning)
* [Regression Analysis](https://en.wikipedia.org/wiki/Regression_analysis)

**Week 3: Case Study in Classification**

* Files
* [Week 3 Slides](https://github.com/fredhutchio/concepts_machine_learning/blob/master/slides/ML_concepts_wk3_slides.pdf)
* Reference
* [Statistical Classification](https://en.wikipedia.org/wiki/Statistical_classification)
* [Binary Classification](https://en.wikipedia.org/wiki/Binary_classification)
* [Confusion Matrix](https://en.wikipedia.org/wiki/Confusion_matrix)
* [Receiver Operating Characteristic (ROC Curve)](https://en.wikipedia.org/wiki/Receiver_operating_characteristic)

**Week 4: Case Study in Deep Learning and Transfer Learning**

* Files
* [Week 4 Slides](https://github.com/fredhutchio/concepts_machine_learning/blob/master/slides/ML_concepts_wk4_slides.pdf)
* Reference
* [Unsupervised Learning](https://en.wikipedia.org/wiki/Unsupervised_learning)
* [Clustering Analysis](https://en.wikipedia.org/wiki/Cluster_analysis)
* [Principal Component Analysis (PCA)](https://en.wikipedia.org/wiki/Principal_component_analysis)
* [Curse of Dimensionality](https://en.wikipedia.org/wiki/Curse_of_dimensionality)
* [Transfer Learning](https://en.wikipedia.org/wiki/Transfer_learning)

**Resources for continued learning**
* Learn about other courses through fredhutch.io [here](http://www.fredhutch.io/resources/). Intermediate Python: Machine Learning and Intermediate R: Machine Learning are technical courses focusing on implementing machine learning methods using Python or R code (please note that Intro to Python/Intro to R, or equivalent understanding of code, is a pre-requisite for the intermediate courses). Intermediate Python: Machine Learning is being pilot tested in November, and Intermediate R: Machine Learning is currently in development.
* The Fred Hutch Bioinformatics and Data Science Cooperative, or the Coop, hosts many community meetings and office hours about data science. Learn more information about these groups [here](https://research.fhcrc.org/coop/en/community/hosted-groups.html),
* Join the [Coop Community Slack](https://join.slack.com/t/fhbig/shared_invite/enQtMzUyMDIxNzk3MDU3LWE5NGUyMTY1NGU0N2VmMmEyNTM5YzM1MmNlMTk2YmM1OWNkMmJiNTQxMTQ4OTNkMTFjMjk3M2Q0MzkwYzQ3NDA) to talk about data science with other Hutch researchers!
* The [Fred Hutch Biomedical Data Science Wiki](https://sciwiki.fredhutch.org) is written by Hutch researchers and staff, and is a great place to find information about data management, bioinformatics, computing, and more.

###### tags: `fredhutch.io` `MachineLearning` `concepts`