Event Phone: 1-610-715-0115

Cancellation Policy: If you cancel your registration at least two weeks before the course is scheduled to begin, you are entitled to a full refund (minus a processing fee of $50).

In the unlikely event that Statistical Horizons LLC must cancel a seminar, we will do our best to inform you as soon as possible of the cancellation. You would then have the option of receiving a full refund of the seminar fee or a credit towards another seminar. In no event shall Statistical Horizons LLC be liable for any incidental or consequential damages that you may incur because of the cancellation.

A 3-Day Remote Seminar Taught by Ross Jacobucci, Ph.D.

Machine learning–including artificial intelligence, big data, supervised learning, and data science–has had an enormous impact in both academic research and industry. Development of innovative machine learning algorithms has been paired with the availability of large datasets. And it has facilitated the collection of even larger datasets, often times containing novel data types (e.g., text).

While machine learning has become increasingly easy to apply in many programming languages, it also presents a number of challenges; specifically, how to interpret the relationships between variables, how to prevent overfitting, and how to deal with the inevitable issues that arise from collecting diverse data types.

This seminar builds off of introductory materials on machine learning, assuming a basic familiarity with the ideas behind regularization in regression, cross-validation, and decision trees. The first day covers the state-of-the-art algorithms for prediction problems with a single outcome. The second day focuses on putting everything together, namely, how to best run all of these algorithms and properly compare their results. Finally, the third day discusses a host of algorithms that were each developed for different types of unsupervised learning tasks. Understanding how each algorithm works will be paired with material on how to apply the method with minimal coding in R.