A high-bias, low-variance introduction to Machine Learning for physicists

Machine Learning (ML) is one of the most exciting and dynamic areas of modern research and application. The purpose of this review is to provide an introduction to the core concepts and tools of machine learning in a manner easily understood and intuitive to physicists. The review begins by covering fundamental concepts in ML and modern statistics such as the bias-variance tradeoff, overfitting, regularization, and generalization before moving on to more advanced topics in both supervised and unsupervised learning. Topics covered in the review include ensemble models, deep learning and neural networks, clustering and data visualization, energy-based models (including MaxEnt models and Restricted Boltzmann Machines), and variational methods. Throughout, we emphasize the many natural connections between ML and statistical physics. A notable aspect of the review is the use of Python notebooks to introduce modern ML/statistical packages to readers using physics-inspired datasets (the Ising Model and Monte-Carlo simulations of supersymmetric decays of proton-proton collisions). We conclude with an extended outlook discussing possible uses of machine learning for furthering our understanding of the physical world as well as open problems in ML where physicists maybe able to contribute. (Notebooks are available here)

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Part 3: Innovation in Clinical Research: AI-based Drug Development Tools and the Regulatory Landscape‍


Part 2: Faster, More Efficient Trials: Novel Trial Designs using Digital Twins‍


Part 1: AI, Digital Twins, and the Future of Clinical Research‍

Learn about how Digital Twins are created and how they are incorporated into clinical trials to increase power, accelerate timelines, and enable patient level insights.
Watch an overview of specific use cases for Digital Twins and learn how novel trial designs with Digital Twins enable smaller trials that maintain their power.
Watch a panel discussion on the regulatory landscape where experts share perspectives on the future of AI-based drug development tools like Digital Twins.