免费书:《机器学习——工程师和科学家的第一课》
2022年由剑桥大学出版社出版
《Machine Learning - A First Course for Engineers and Scientists | sml-book-page》Andreas Lindholm, Niklas Wahlström, Fredrik Lindsten, and Thomas B. Schön
电子版下载地址:
https://event-cdn.baai.ac.cn/file/file-browser/S3wpKDDbFxnkT448mMyZsYwsWiRDRGBk.pdf
Table of Contents
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Introduction
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The machine learning problem
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Machine learning concepts via examples
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About this book
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Supervised machine learning: a first approach
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Supervised machine learning
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A distance-based method: k-NN
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A rule-based method: Decision trees
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Basic parametric models for regression and classification
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Linear regression
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Classification and logistic regression
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Polynomial regression and regularization
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Generalized linear models
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Understanding, evaluating and improving the performance
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Expected new data error: performance in production
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Estimating the expected new data error
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The training error–generalization gap decomposition
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The bias-variance decomposition
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Additional tools for evaluating binary classifiers
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Learning parametric models
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Principles pf parametric modelling
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Loss functions and likelihood-based models
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Regularization
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Parameter optimization
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Optimization with large datasets
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Hyperparameter optimization
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Neural networks and deep learning
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The neural network model
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Training a neural network
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Convolutional neural networks
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Dropout
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Ensemble methods: Bagging and boosting
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Bagging
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Random forests
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Boosting and AdaBoost
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Gradient boosting
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Nonlinear input transformations and kernels
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Creating features by nonlinear input transformations
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Kernel ridge regdression
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Support vector regression
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Kernel theory
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Support vector classification
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The Bayesian approach and Gaussian processes
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The Bayesian idea
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Bayesian linear regression
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The Gaussian process Online material: Gaussian process visualization
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Practial aspects of the Gaussian process
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Other Bayesian methods in machine learning
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Generative models and learning from unlabeled data
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The Gaussian mixture model and discriminant analysis
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Cluster analysis
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Deep generative models
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Representation learning and dimensionality reduction
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User aspects of machine learning
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Defining the machine learning problem
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Improving a machine learning model
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What if we cannot collect more data?
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Practical data issues
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Can I trust my machine learning model?
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Ethics in machine learning (by David Sumpter)
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Fairness and error functions
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Misleading claims about performance
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Limitations of training data
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