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 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 assumes a basic familiarity with machine learning and covers statistical machine learning, Bayesian machine learning, kernel methods and Gaussian processes, Bayesian probabilistic programming with MCMC and Variational Inference, Bayesian Additive Regression Trees, deep generative modeling with Variational Autoencoders, Convolution Neural Networks, and Recurrent Neural Networks.

Machine Learning methods have gained much attention for their applicability to large and complex datasets: large in terms of the number of observations and/or the number of variables, complex in terms of structure: inputs and / or outputs which are vector-valued, text, images, and more. While a vast array of off-the-shelf learning methods and models are available for simple classification and regression tasks, more complex problems require a deeper understanding of the strengths, weaknesses, and principles underlying various approaches, and how they can fit together to solve real-world problems.

The first day introduces statistical machine learning, Bayesian machine learning, and probabilistic programming. The second day focuses on Gaussian processes, kernel methods, and neural networks. The third day goes into depth on deep learning, covering Variational Autoencoders, Convolution Neural Networks, and Recurrent Neural Networks. Throughout the course, you will gain experience with these methods through hands-on exercises.