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

  1. Introduction

    • The machine learning problem

    • Machine learning concepts via examples

    • About this book

  2. Supervised machine learning: a first approach

    • Supervised machine learning

    • A distance-based method: k-NN

    • A rule-based method: Decision trees

  3. Basic parametric models for regression and classification

    • Linear regression

    • Classification and logistic regression

    • Polynomial regression and regularization

    • Generalized linear models

  4. Understanding, evaluating and improving the performance

    • Expected new data error: performance in production

    • Estimating the expected new data error

    • The training error–generalization gap decomposition

    • The bias-variance decomposition

    • Additional tools for evaluating binary classifiers

  5. Learning parametric models

    • Principles pf parametric modelling

    • Loss functions and likelihood-based models

    • Regularization

    • Parameter optimization

    • Optimization with large datasets

    • Hyperparameter optimization

  6. Neural networks and deep learning

    • The neural network model

    • Training a neural network

    • Convolutional neural networks

    • Dropout

  7. Ensemble methods: Bagging and boosting

    • Bagging

    • Random forests

    • Boosting and AdaBoost

    • Gradient boosting

  8. Nonlinear input transformations and kernels

    • Creating features by nonlinear input transformations

    • Kernel ridge regdression

    • Support vector regression

    • Kernel theory

    • Support vector classification

  9. The Bayesian approach and Gaussian processes

  10. Generative models and learning from unlabeled data

    • The Gaussian mixture model and discriminant analysis

    • Cluster analysis

    • Deep generative models

    • Representation learning and dimensionality reduction

  11. User aspects of machine learning

    • Defining the machine learning problem

    • Improving a machine learning model

    • What if we cannot collect more data?

    • Practical data issues

    • Can I trust my machine learning model?

  12. Ethics in machine learning (by David Sumpter)

    • Fairness and error functions

    • Misleading claims about performance

    • Limitations of training data

 

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