If so, this course is for you!
Join our Bayesian Machine Learning course and empower your data-driven decisions—no matter your field!
Dive into the world of Bayesian data analysis! In this hands-on course, you’ll learn how to build, infer, and interpret probabilistic models using Stan — the state-of-the-art platform for statistical modeling and high-performance computation.
Level: Intermediate (basic statistics & programming assumed)
Curious about what makes Bayesian Machine Learning unique? Watch this short 13-minute presentation by Prof. Ezequiel Alvarez to get inspired and discover why Bayesian approaches are so powerful and essential for real-world data analysis.
Bayesian Machine Learning consists of combining theory, software, and a series of statistical techniques developed in recent years, to a system or problem, in order to maximize understanding based on the observed data.
Within this framework, a real-world system is modeled as a probabilistic model in which the system's data are sampled from a probability density function (PDF). This task is part art and part craft, requiring not only statistical knowledge but, above all, a deep understanding of the specific problem at hand. In fact, a crucial aspect of this art and craft involves astutely modeling the system to explicitly reveal internal variables that are not directly observed (latent variables), but about which we have prior knowledge that can be exploited in the form of priors. The framework is completed by deploying the idea and theory onto suitable probabilistic software, in which the expressions are defined over random variables instead of deterministic variables. The execution of such a script relies on Bayes' theorem to return the user a posterior probability distribution over the parameters and latent variables, which are connected to the real parameters of the system under study.
In this way, modeling, implementation, and execution within the Bayesian Machine Learning framework can provide a unique understanding of the problem based on the observation of the data. This understanding can be exploited in various ways, such as measuring parameters of the problem, determining relationships and dependencies between internal parameters, identifying the composition of a data sample, detecting anomalies in the data, sampling synthetic data from the system, and in particular, gaining a deeper and more detailed understanding of the system, among other applications.
Last but not least, you can generally expect Bayesian Machine Learning to outperform Neural Network analyses for several reasons. One key advantage of Bayesian ML is its ability to incorporate prior knowledge, which is extremely valuable. In Bayesian ML, you can obtain meaningful results and draw key conclusions with relatively few data points—ranging from about 50 to 10,000—a region in which Neural Networks typically do not have enough data to even begin learning effectively.
Real World problems don't have much data and prior info is crucial.... this is why Bayesian ML is the way!
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