縮小範圍

商品類型

原文書（20）

商品狀況

可訂購商品（20）

庫存狀況

無庫存（20）

商品定價

$600~$799 （1）

$800以上（19）

出版日期

2022~2023 （1）

2020~2021 （1）

2018~2019 （4）

2016~2017 （2）

2016年以前（12）

裝訂方式

平裝（5）

精裝（15）

作者

Ethem Alpaydin （2）

Kevin P. Murphy （2）

Robert E. Schapire, Yoav Freund （2）

Albert Bifet, Gavald Ricard, Geoffrey Holmes, Bernhard Pfahringer （1）

Carl Edward Rasmussen, Christopher K. I. Williams （1）

出版社/品牌

Mit Pr （17）

Bradford Books （3）

三民網路書店 / 搜尋結果

搜尋條件

商品類型

商品狀況

庫存狀況

商品定價

出版日期

裝訂方式

作者

出版社/品牌

關閉

共20筆商品，1/1頁

Causation, Prediction, and Search

作者：Peter Spirtes; Clark Glymour; Richard Scheines 出版社：Bradford Books 出版日：2001/01/29 裝訂：平裝

What assumptions and methods allow us to turn observations into causal knowledge, and how can even incomplete causal knowledge be used in planning and prediction to influence and control our environme

到貨時通知我

缺貨無法訂購

Introduction to Machine Learning

作者：Ethem Alpaydin 出版社：Mit Pr 出版日：2009/12/04 裝訂：精裝

The goal of machine learning is to program computers to use example data or past experience to solve a given problem. Many successful applications of machine learning exist already, including systems

到貨時通知我

缺貨無法訂購

Boosting―Foundations and Algorithms

作者：Robert E. Schapire; Yoav Freund 出版社：Mit Pr 出版日：2012/05/18 裝訂：精裝

Boosting is an approach to machine learning based on the idea of creating a highlyaccurate predictor by combining many weak and inaccurate "rules of thumb." A remarkablyrich theory has evolved around

到貨時通知我

缺貨無法訂購

Machine Learning in Non-Stationary Environments ─ Introduction to Covariate Shift Adaptation

作者：Masashi Sugiyama; Motoaki Kawanabe 出版社：Mit Pr 出版日：2012/03/30 裝訂：精裝

As the power of computing has grown over the past few decades, the field of machinelearning has advanced rapidly in both theory and practice. Machine learning methods are usuallybased on the assumptio

到貨時通知我

缺貨無法訂購

Foundations of Machine Learning

作者：Mehryar Mohri; Afshin Rostamizadeh; Ameet Talwalkar 出版社：Mit Pr 出版日：2012/08/17 裝訂：精裝

This graduate-level textbook introduces fundamental concepts and methods in machinelearning. It describes several important modern algorithms, provides the theoretical underpinningsof these algorithms

到貨時通知我

缺貨無法訂購

79折

Machine Learning ─ A Probabilistic Perspective

作者：Kevin P. Murphy 出版社：Mit Pr 出版日：2012/08/24 裝訂：精裝

Today's Web-enabled deluge of electronic data calls for automated methods of dataanalysis. Machine learning provides these, developing methods that can automatically detect patternsin data and then us

定價：6050 元，優惠價：79 4780

無庫存，下單後進貨(到貨天數約30-45天)

79折

Introduction to Natural Language Processing

作者：Jacob Eisenstein 出版社：Mit Pr 出版日：2019/10/01 裝訂：精裝

A survey of computational methods for understanding, generating, and manipulating human language, which offers a synthesis of classical representations and algorithms with contemporary machine learnin

定價：4125 元，優惠價：79 3259

無庫存，下單後進貨(到貨天數約30-45天)

79折

Bioinformatics ─ The Machine Learning Approach

作者：Pierre Baldi; Soren Brunak 出版社：Bradford Books 出版日：2001/07/20 裝訂：精裝

A guide to machine learning approaches and their application to the analysis ofbiological data.

定價：2800 元，優惠價：79 2212

無庫存，下單後進貨(到貨天數約30-45天)

79折

Gaussian Processes for Machine Learning

作者：Carl Edward Rasmussen; Christopher K. I. Williams 出版社：Mit Pr 出版日：2005/11/23 裝訂：精裝

Gaussian processes (GPs) provide a principled, practical, probabilistic approach to learning in kernel machines. GPs have received increased attention in the machine-learning community over the past d

定價：2750 元，優惠價：79 2173

無庫存，下單後進貨(到貨天數約30-45天)

Reinforcement Learning ― An Introduction

79折

10.

Reinforcement Learning ― An Introduction

作者：Richard S. Sutton; Andrew G. Barto; Francis Bach 出版社：Bradford Books 出版日：2018/11/13 裝訂：精裝

The significantly expanded and updated new edition of a widely used text on reinforcement learning, one of the most active research areas in artificial intelligence.Reinforcement learning, one of the

定價：5500 元，優惠價：79 4345

無庫存，下單後進貨(到貨天數約30-45天)

Machine Learning for Data Streams ― With Practical Examples in Moa

79折

11.

Machine Learning for Data Streams ― With Practical Examples in Moa

作者：Albert Bifet; Gavald Ricard; Geoffrey Holmes; Bernhard Pfahringer 出版社：Mit Pr 出版日：2018/03/02 裝訂：精裝

A hands-on approach to tasks and techniques in data stream mining and real-time analytics, with examples in MOA, a popular freely available open-source software framework.Today many information source

定價：3025 元，優惠價：79 2390

無庫存，下單後進貨(到貨天數約30-45天)

Elements of Causal Inference ─ Foundations and Learning Algorithms

79折

12.

Elements of Causal Inference ─ Foundations and Learning Algorithms

作者：Jonas Peters; Dominik Janzing; Bernhard Sch?女opf 出版社：Mit Pr 出版日：2017/11/29 裝訂：精裝

The mathematization of causality is a relatively recent development, and has become increasingly important in data science and machine learning. This book offers a self-contained and concise introduct

定價：2475 元，優惠價：79 1955

無庫存，下單後進貨(到貨天數約30-45天)

79折

13.

Deep Learning

作者：Ian Goodfellow; Yoshua Bengio; Aaron Courville 出版社：Mit Pr 出版日：2016/11/18 裝訂：精裝

The subject of this textbook is deep learning, the modern incarnation of neural networks. This is the first textbook on this subject written by recognized academic author

定價：5500 元，優惠價：79 4345

無庫存，下單後進貨(到貨天數約30-45天)

14.

Semi-Supervised Learning

作者：Olivier Chapelle; Bernhard Scholkopf; Alexander Zien 出版社：Mit Pr 出版日：2010/01/22 裝訂：平裝

In the field of machine learning, semi-supervised learning (SSL) occupies the middle ground, between supervised learning (in which all training examples are labeled) and unsupervised learning (in whi

到貨時通知我

缺貨無法訂購

15.

The Minimum Description Length Principle

作者：Peter D. Grwald 出版社：Mit Pr 出版日：2007/03/23 裝訂：平裝

A comprehensive introduction and reference guide to the minimum description length (MDL) Principle that is accessible to researchers dealing with inductive reference in diverse areas including statist

到貨時通知我

缺貨無法訂購

79折

16.

Boosting ─ Foundations and Algorithms

作者：Robert E. Schapire; Yoav Freund 出版社：Mit Pr 出版日：2014/01/10 裝訂：平裝

Boosting is an approach to machine learning based on the idea of creating a highlyaccurate predictor by combining many weak and inaccurate "rules of thumb." A remarkablyrich theory has evolved around

定價：2750 元，優惠價：79 2173

無庫存，下單後進貨(到貨天數約30-45天)

17.

Introduction to Statistical Relational Learning

作者：Lise Getoor ; Ben Taskar ; Daphne Koller; Nir Friedman; Lise Getoor 出版社：Mit Pr 出版日：2019/09/22 裝訂：平裝

Advanced statistical modeling and knowledge representation techniques for a newly emerging area of machine learning and probabilistic reasoning; includes introductory material, tutorials for different

到貨時通知我

缺貨無法訂購

18.

Introduction to Machine Learning

作者：Ethem Alpaydin 出版社：Mit Pr 出版日：2014/08/22 裝訂：精裝

The goal of machine learning is to program computers to use example data or pastexperience to solve a given problem. Many successful applications of machine learning exist already,including systems th

到貨時通知我

缺貨無法訂購

79折

19.

Introduction to Machine Learning

作者：Ethem Alpaydin (OEzyegin University) 出版社：Mit Pr 出版日：2020/03/24 裝訂：精裝

定價：3575 元，優惠價：79 2824

無庫存，下單後進貨(到貨天數約30-45天)

79折

20.

Probabilistic Machine Learning

作者：Kevin P. Murphy 出版社：Mit Pr 出版日：2022/02/01 裝訂：精裝

A detailed and up-to-date introduction to machine learning, presented through the unifying lens of probabilistic modeling and Bayesian decision theory.This book offers a detailed and up-to-date introduction to machine learning (including deep learning) through the unifying lens of probabilistic modeling and Bayesian decision theory. The book covers mathematical background (including linear algebra and optimization), basic supervised learning (including linear and logistic regression and deep neural networks), as well as more advanced topics (including transfer learning and unsupervised learning). End-of-chapter exercises allow students to apply what they have learned, and an appendix covers notation. Probabilistic Machine Learning grew out of the author’s 2012 book, Machine Learning: A Probabilistic Perspective. More than just a simple update, this is a completely new book that reflects the dramatic developments in the field since 2012, most notably deep learning. In addition, the

定價：6875 元，優惠價：79 5431

無庫存，下單後進貨(到貨天數約30-45天)

共20 筆
1 頁

熱搜：

佛法是科學的終極典範

巨人思維

老公怎麼還不去死

DragonMasters 1-23

理想的讀本

法律與人生(鄰居篇)

走向馬克思主義史學之路

永續發展相關規範與實務

為妳煮食

新基本小六法

縮小範圍

三民網路書店 / 搜尋結果

搜尋條件

Causation, Prediction, and Search

Introduction to Machine Learning

Boosting―Foundations and Algorithms

Machine Learning in Non-Stationary Environments ─ Introduction to Covariate Shift Adaptation

Foundations of Machine Learning

Machine Learning ─ A Probabilistic Perspective

Introduction to Natural Language Processing

Bioinformatics ─ The Machine Learning Approach

Gaussian Processes for Machine Learning

Reinforcement Learning ― An Introduction

Machine Learning for Data Streams ― With Practical Examples in Moa

Elements of Causal Inference ─ Foundations and Learning Algorithms

Deep Learning

Semi-Supervised Learning

The Minimum Description Length Principle

Boosting ─ Foundations and Algorithms

Introduction to Statistical Relational Learning

Introduction to Machine Learning

Introduction to Machine Learning

Probabilistic Machine Learning