Statistical Learning: Dimension Reduction

Learning Objectives

• Recognize the potential impact of the curse of dimensionality in statistical learning problems.
• Apply PCA as a dimension reduction technique in R.
• Apply the concept of non-negative matrix factorization.
• Identify the appropriate type of machine learning algorithm based on problem description.

SESSION MATERIALS

The instructor will present using an R notebook that integrates exposition with executable code examples, exercises, and quizzes.

You can run your own copy of the notebook in a web browser using Google Colab. No further software installation is required.

To view the notebook: click on the link below

To run notebook code: you must be signed in to a Google account and click “run anyway” if prompted.

• View the notebook in Google Colab

Recorded Session

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#TAMIDSBiomedicalDataScience