Traditional communication systems design is dominated by methods that are based on statistical models. These statistical-model-based algorithms, which we refer to henceforth as model-based methods, rely on mathematical models that describe the transmission process, signal propagation, receiver noise, interference, and many other components of the system that affect the end-to-end signal transmission and reception. Such mathematical models use parameters that vary over time as the channel conditions, the environment, network traffic, or network topology change. Therefore, for optimal operation, many of the algorithms used in communication systems rely on the underlying mathematical models as well as the estimation of the model parameters. However, there are cases where this approach fails, in particular when the mathematical models for one or more of the system components are highly complex, hard to estimate, poorly understood, do not well-capture the underlying physics of the system, or do not lend themselves to computationally-efficient algorithms.

More generally, one may regard the entire communication system design as an end-to-end reconstruction task and jointly optimize transmitter and receiver NNs [1]. Both traditional [2-4] and end-to-end learning [5-7] have received considerable attention for optical fiber systems. However, the reliance on NNs as universal (but sometimes poorly understood) function approximators makes it difficult to incorporate existing domain knowledge or interpret the obtained solutions. Rather than relying on NNs, a different approach is to start from an existing model and parameterize it. For fiberoptic systems, this can be done for example by considering the split-step method (SSM) for numerically solving the nonlinear Schr odinger equation (NLSE).

In this recorded webcast, Daniel Emaasit introduces model-based machine learning and related concepts, practices and tools such as Bayes' Theorem, probabilistic programming, and RStan. The field of machine learning has seen the development of thousands of learning algorithms. Typically, scientists choose from these algorithms to solve specific problems. Their choices often being limited by their familiarity with these algorithms. In this classical/traditional framework of machine learning, scientists are constrained to making some assumptions so as to use an existing algorithm.

This guest post was written by Daniel Emaasit, a Ph.D Student of Transportation Engineering at the University of Nevada, Las Vegas. Daniel's research interests include the development of probabilistic machine learning methods for high-dimensional data, with applications to urban mobility, transport planning, highway safety, & traffic operations. Don't miss Daniel's webinar on Model-Based Machine Learning and Probabilistic Programming using RStan, scheduled for July 20, 2016 at 11:00 AM PST. This blog post follows my journey from traditional statistical modeling to Machine Learning (ML) and introduces a new paradigm of ML called Model-Based Machine Learning (Bishop, 2013). Model-Based Machine Learning may be of particular interest to statisticians, engineers, or related professionals looking to implement machine learning in their research or practice.

In the last several decades, thousands of machine learning algorithms have been developed. Very often, the selection of an algorithm to solve a particular problem is driven more by the data scientist's familiarity with a small subset of available algorithms, than optimizing for predictive power or operational constraints. This is unsurprising: Newcomers to machine learning and veteran data scientists alike, may be overwhelmed by the multitude of machine learning algorithms and where and how it is most appropriate to use them. In this webinar, Daniel Emaasit will introduce Model-Based Machine Learning (MBML), an approach to machine learning which addresses these challenges. Daniel will discuss the various uses of MBML, from tasks such as classification, to regression and clustering, and how it allows data scientists to address the uncretainty inherent to real-world machine learning applications.

This guest post was written by Daniel Emaasit, a Ph.D Student of Transportation Engineering at the University of Nevada, Las Vegas. Daniel's research interests include the development of probabilistic machine learning methods for high-dimensional data, with applications to urban mobility, transport planning, highway safety, & traffic operations. Don't miss Daniel's webinar on Model-Based Machine Learning and Probabilistic Programming using RStan, scheduled for July 20, 2016 at 11:00 AM PST. This blog post follows my journey from traditional statistical modeling to Machine Learning (ML) and introduces a new paradigm of ML called Model-Based Machine Learning (Bishop, 2013). Model-Based Machine Learning may be of particular interest to statisticians, engineers, or related professionals looking to implement machine learning in their research or practice. During my Masters in Transportation Engineering (2011-2013), I used traditional statistical modeling in my research to study transportation related problems such as highway crashes.

Today machine learning is centre stage in the world of technology, and thousands of scientists and engineers are applying machine learning to an extraordinarily broad range of domains. However, making effective use of machine learning in practice can be daunting, especially for newcomers to the field. Over the last five decades, researchers have created literally thousands of machine learning algorithms. Traditionally an engineer wanting to solve a problem using machine learning must choose one or more of these algorithms to try, often constrained those algorithms they happen to be familiar with, or by the availability of software implementations. In this talk we view machine learning from a fresh perspective which we call'model-based machine learning', in which a bespoke solution is formulated for each new application.