Machine Learning Algorithms in R - A Deep Dive

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Proposal
Long Form
Intermediate

Excerpt

As a major benchmark and trend-setter in machine learning and statistics, R, a free and open source statistical computing language, has much to offer to anyone interested in machine learning, statistics, or numerical computing. In this tutorial, I will share with the audience the vast ecosystem around R, and get the listeners started right away with some of the most widely used machine learning algorithms. You don't have to be a statistician or computer scientist to use R - its concise syntax and expressive nature will only make you want to use it more and more for machine learning and other computing tasks!

Description

This tutorial is a practical examination into some of the most popular algorithms in machine learning, such as generalized linear modeling (GLM), random forest(RF), and neural network, with the critical tool: R!

First, the audience will have a high-level overview of each model, and then learn about the useful parameters in these algorithms, how to tune these parameters to best solve these problems, and the accompanying R packages that implement these algorithms. Time permitting, we will also look at different R packages that implemented each algorithm, understand the techniques and engineering choices behind the differences, and talk about how to utilize each implementation for our purposes.

We will cover three algorithms, and if time permitting, more*! Each algorithm will have its own section. The audience will walk away from the presentation feeling confident to apply machine learning algorithms to solve problems in R.

Some background knowledge in machine learning and statistics will be helpful, but not required.

*Time permitting, we will also look at one or two algorithms from the following: gradient boosting, naive Bayes, K-nearest-neighbor, K-Means, or one of the classic hierachical clustering algorithms.

Tags

machine learning, algorithms, Implementation, R, random forest, practice, tutorial, neural network, generalized linear model

Speaking experience

Public speaking experiences in the past 5 years from smaller groups of 10+ people to 100+ people in different environments(e.g. educational, corporate, workshops, panels, etc.) in a range of topics from writing and diversity to coding, mathematics, and astronomy. I have given this talk before.

Speaker

  • Biography

    Trained in Mathematics and Statistics, Helen is a data scientist and machine learning researcher with a passion for security, machine learning, and free and open source technology. Helen has worked on exciting projects in management consulting, technology start-ups, and non-profits in Asia, North America, and Europe. Helen has also taught mathematics, coding, writing, and astronomy to different audiences. When not writing code, building things, or sharing knowledge, she enjoys learning new spoken languages, photography, and long-distance running. She has many stories to tell, and you are welcome to ask her in person :)

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