从算法视角看机器学习（第二版），编号14665832...

简介：
Title: Machine Learning: An Algorithmic Perspective, 2nd Edition Author: Stephen Marsland Length: 457 pages Edition: 2 Language: English Publisher: Chapman and Hall/CRCPublication Date: 2014-10-08 ISBN-10: 1466583282 ISBN-13: 9781466583283 A Robust and Practical Approach for Students with Limited Statistical Knowledge. Following the release of the highly successful initial edition, the field of machine learning has witnessed considerable advancements, particularly in the examination of statistical interpretations underlying machine learning algorithms. However, computer science students lacking a solid foundation in statistics frequently encounter difficulties when initiating their studies within this domain. To address this shortcoming, *Machine Learning: An Algorithmic Perspective*, Second Edition, equips students with a comprehensive understanding of the core algorithms driving machine learning. It guides them toward mastering essential mathematics and statistics, alongside the requisite programming skills and experimental practices. New Features in the Second Edition: Two newly incorporated chapters delve into the intricacies of deep belief networks and Gaussian processes. Furthermore, the structure of the chapters has been revised to ensure a more intuitive progression of content. The material pertaining to support vector machines has undergone a thorough revision, including a straightforward implementation designed for practical experimentation. Additional content includes explorations of random forests, the perceptron convergence theorem, accuracy methods, and conjugate gradient optimization specifically tailored for multi-layer perceptrons. Expanded discussions are provided regarding Kalman and particle filters, alongside improved code incorporating enhanced naming conventions within Python. This text is suitable for both introductory courses delivered over a single semester and more advanced academic settings; it actively encourages students to engage with hands-on coding exercises. Each chapter features detailed examples complemented by supplementary reading materials and challenging problems. The complete code utilized in generating these illustrative examples is readily accessible via the author’s website. Table of Contents: Chapter 1: Introduction Chapter 2: Preliminaries Chapter 3: Neurons, Neural Networks, and Linear Discriminants Chapter 4: The Multi-layer Perceptron Chapter 5: Radial Basis Functions and Splines Chapter 6: Dimensionality Reduction Chapter 7: Probabilistic Learning Chapter 8: Support Vector Machines Chapter 9: Optimization and Search Chapter 10: Evolutionary Learning Chapter 11: Reinforcement Learning Chapter 12: Learning with Trees Chapter 13: Decision by Committee: Ensemble Learning Chapter 14: Unsupervised Learning Chapter 15: Markov Chain Monte Carlo (MCMC) Methods Chapter 16: Graphical Models Chapter 17: Symmetric Weights and Deep Belief Networks Chapter 18: Gaussian Processes