
A highbias, lowvariance introduction to Machine Learning for physicists
Machine Learning (ML) is one of the most exciting and dynamic areas of m...
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BlinkML: Efficient Maximum Likelihood Estimation with Probabilistic Guarantees
The rising volume of datasets has made training machine learning (ML) mo...
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Introduction to RareEvent Predictive Modeling for Inferential Statisticians – A HandsOn Application in the Prediction of Breakthrough Patents
Recent years have seen a substantial development of quantitative methods...
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A brief introduction to the Grey Machine Learning
This paper presents a brief introduction to the key points of the Grey M...
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Understanding Kernel Ridge Regression: Common behaviors from simple functions to density functionals
Accurate approximations to density functionals have recently been obtain...
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A Brief Introduction to Machine Learning for Engineers
This monograph aims at providing an introduction to key concepts, algori...
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Introduction to Machine Learning for the Sciences
This is an introductory machine learning course specifically developed w...
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Introduction to Machine Learning for Accelerator Physics
This pair of CAS lectures gives an introduction for accelerator physics students to the framework and terminology of machine learning (ML). We start by introducing the language of ML through a simple example of linear regression, including a probabilistic perspective to introduce the concepts of maximum likelihood estimation (MLE) and maximum a priori (MAP) estimation. We then apply the concepts to examples of neural networks and logistic regression. Next we introduce nonparametric models and the kernel method and give a brief introduction to two other machine learning paradigms, unsupervised and reinforcement learning. Finally we close with example applications of ML at a freeelectron laser.
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