Getting better at R: Learning More Packages More Deeply & Hyper-parameter Tuning

Talk 1:
Speaker: Daniel Jacobs (@djacobs7), Senior Director, Data Science Engineering at Age Of Learning. Website: www.dontyouremember.com

Title: Getting better at R By Learning More Packages More Deeply

Abstract:

Learning R means learning dozens of different packages and methods. CRAN has over 10,000 packages, and it's not always clear what's important to learn next. Once you decide what to learn, it's can be difficult to commit the important methods to memory so that you can use them easily over the next weeks or months. This talk walks you through strategies for deciding what to learn, and then committing them to memory with the spaced repetition algorithm. It gives an example of discovering what packages to learn via code analysis. Finally, it describes the development of the remembR package, which lets you study R directly in R.

Talk 2:
Speaker: Emil Hvitfeldt (@Emil_Hvitfeldt), Research Programmer at USC and Co-organizer of @laeRusers

Title: Hyper-parameter Tuning with Tune Package

Abstract:
Many statistical and machine learning models have parameters that can directly be inferred from the data. The newly released tune package is part of the tidymodels framework and allows you to do hyper-parameter tuning.

Code of Conduct: https://github.com/laRusers/codeofconduct

Timeline:
6:30: Socializing
7:00-8:00: Talks
8:00: Socializing

Address:
Room 115/116
USC
2001 N. Soto St.
Los Angeles, CA 90032

Parking: Parking is free in the metered spots on the east side of the parking lot after 5 PM

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Find our previous talks on GitHub: https://github.com/laRusers/presentations
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