Machine Learning – August 2022

Event Phone: 1-610-715-0115

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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 4-Day Livestream Seminar Taught by Seth Flaxman, Ph.D.

Machine learning has emerged as a major field at the intersection of statistics and computer science where the goal is to create reliable and flexible predictive models. This seminar offers a thorough introduction to supervised machine learning methods. Topics covered include: supervised learning; loss functions and optimization; cross-validation; the bias/variance tradeoff; high-dimensional variable selection and regularization methods; an overview of the R programming language; non-linear methods such as random forests and support vector machines; and an introduction to deep learning.

Machine Learning methods have gained much attention for their applicability to large datasets: large in terms of the number of observations and / or the number of variables. While a vast selection of learning methods and models are available, almost all can be framed in terms of finding parameters to minimize a loss function. A small set of general principles ensures good performance.

This course will introduce machine learning, R, and RStudio. It will also focus on model selection, variable selection, and regularization with the goal of building robust models with good generalization performance. The seminar will focus on non-linear methods, including support vector machines, random forest, and deep learning. Throughout the course, you will gain experience with these methods through hands-on exercises.

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