How can a machine learn from experience? Probabilistic modelling provides a framework
for understanding what learning is, and has therefore emerged as one of the principal …

Several decades of research in the field of machine learning have resulted in a multitude of
different algorithms for solving a broad range of problems. To tackle a new application, a …

The engineering of machine learning systems is still a nascent field; relying on a seemingly
daunting collection of quickly evolving tools and best practices. It is our hope that this …

Probabilistic models aim to explain human cognition by appealing to the principles of
probability theory and statistics, which dictate how an agent should act rationally in …

A detailed and up-to-date introduction to machine learning, presented through the unifying
lens of probabilistic modeling and Bayesian decision theory. This book offers a detailed and …

As artificial intelligence is being increasingly used for high-stakes applications, it is
becoming more and more important that the models used be interpretable. Bayesian …

Probability is the bedrock of machine learning. You cannot develop a deep understanding
and application of machine learning without it. Cut through the equations, Greek letters, and …

Probability as an Alternative to Boolean Logic While logic is the mathematical foundation of
rational reasoning and the fundamental principle of computing, it is restricted to problems …

Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best suited for optimization over continuous domains of …

Bayesian optimization is an approach to optimizing objective functions that take a long time
(minutes or hours) to evaluate. It is best-suited for optimization over continuous domains of …