Machine Learning Refined: Foundations, Algorithms, and Applications, 2/e (Hardcover)
暫譯: 精煉機器學習：基礎、演算法與應用，第二版（精裝本）

Watt, Jeremy, Borhani, Reza, Katsaggelos, Aggelos

Machine Learning Refined: Foundations, Algorithms, and Applications, 2/e (Hardcover)
  • 出版商: Cambridge
  • 出版日期: 2020-01-09
  • 售價: $1,680
  • 貴賓價: 9.8 折 $1,646
  • 語言: 英文
  • 頁數: 594
  • 裝訂: Hardcover - also called cloth, retail trade, or trade
  • ISBN: 1108480721
  • ISBN-13: 9781108480727
  • 相關分類: Machine Learning

    • 立即出貨 (庫存=1)

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商品描述

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the essential mathematical prerequisites, to offer a fresh and accessible way to learn. Practical applications are emphasized, with examples from disciplines including computer vision, natural language processing, economics, neuroscience, recommender systems, physics, and biology. Over 300 color illustrations are included and have been meticulously designed to enable an intuitive grasp of technical concepts, and over 100 in-depth coding exercises (in Python) provide a real understanding of crucial machine learning algorithms. A suite of online resources including sample code, data sets, interactive lecture slides, and a solutions manual are provided online, making this an ideal text both for graduate courses on machine learning and for individual reference and self-study.

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  • Encourages geometric intuition and algorithmic thinking to provide an intuitive understanding of key concepts and an interactive way of learning
  • Features coding exercises for Python to help put knowledge into practice
  • Emphasizes practical applications, with real-world examples, to give students the confidence to conduct research, build products, and solve problems
  • Completely self-contained, with appendices covering the essential mathematical prerequisites

商品描述(中文翻譯)

這本書以直觀而嚴謹的方式介紹機器學習，為學生提供進行研究和構建數據驅動產品所需的基本知識和實用工具。作者優先考慮幾何直覺和算法思維，並詳細說明所有必要的數學前提，以提供一種新穎且易於理解的學習方式。書中強調實際應用，並提供來自計算機視覺、自然語言處理、經濟學、神經科學、推薦系統、物理學和生物學等學科的範例。書中包含超過300幅彩色插圖，這些插圖經過精心設計，以便直觀理解技術概念，並提供超過100個深入的編碼練習（使用Python），以幫助讀者真正理解關鍵的機器學習算法。還提供一系列在線資源，包括示例代碼、數據集、互動講義幻燈片和解答手冊，使這本書成為研究生機器學習課程和個人參考及自學的理想教材。

- 鼓勵幾何直覺和算法思維，以提供對關鍵概念的直觀理解和互動學習方式
- 提供Python編碼練習，幫助將知識付諸實踐
- 強調實際應用，通過真實世界的範例，讓學生有信心進行研究、構建產品和解決問題
- 完全自足，附錄涵蓋必要的數學前提知識

作者簡介

Jeremy Watt, Northwestern University, Illinois
Jeremy Watt received his Ph.D. in Electrical Engineering from Northwestern University, Illinois, and is now a machine learning consultant and educator. He teaches machine learning, deep learning, mathematical optimization, and reinforcement learning at Northwestern University, Illinois.

Reza Borhani, Northwestern University, Illinois
Reza Borhani received his Ph.D. in Electrical Engineering from Northwestern University, Illinois, and is now a machine learning consultant and educator. He teaches a variety of courses in machine learning and deep learning at Northwestern University, Illinois.

Aggelos Katsaggelos, Northwestern University, Illinois
Aggelos K. Katsaggelos is the Joseph Cummings Professor at Northwestern University, Illinois, where he heads the Image and Video Processing Laboratory. He is a Fellow of Institute of Electrical and Electronics Engineers (IEEE), SPIE, the European Association for Signal Processing (EURASIP), and The Optical Society (OSA) and the recipient of the IEEE Third Millennium Medal (2000).

作者簡介(中文翻譯)

傑瑞米·瓦特，伊利諾伊州西北大學

傑瑞米·瓦特在伊利諾伊州西北大學獲得電機工程博士學位，目前是一名機器學習顧問和教育者。他在伊利諾伊州西北大學教授機器學習、深度學習、數學優化和強化學習課程。

瑞扎·博哈尼，伊利諾伊州西北大學

瑞扎·博哈尼在伊利諾伊州西北大學獲得電機工程博士學位，目前是一名機器學習顧問和教育者。他在伊利諾伊州西北大學教授各種機器學習和深度學習課程。

阿基洛斯·卡查戈洛斯，伊利諾伊州西北大學

阿基洛斯·K·卡查戈洛斯是伊利諾伊州西北大學的約瑟夫·卡明斯教授，負責影像與視頻處理實驗室。他是電機電子工程師學會（IEEE）、SPIE、歐洲信號處理協會（EURASIP）和光學學會（OSA）的會士，並於2000年獲得IEEE第三千年獎章。

目錄大綱

1. Introduction to machine learning
Part I. Mathematical Optimization:
2. Zero order optimization techniques
3. First order methods
4. Second order optimization techniques
Part II. Linear Learning:
5. Linear regression
6. Linear two-class classification
7. Linear multi-class classification
8. Linear unsupervised learning
9. Feature engineering and selection
Part III. Nonlinear Learning:
10. Principles of nonlinear feature engineering
11. Principles of feature learning
12. Kernel methods
13. Fully-connected neural networks
14. Tree-based learners
Part IV. Appendices: Appendix A. Advanced first and second order optimization methods
Appendix B. Derivatives and automatic differentiation

目錄大綱(中文翻譯)

1. Introduction to machine learning

Part I. Mathematical Optimization:

2. Zero order optimization techniques

3. First order methods

4. Second order optimization techniques

Part II. Linear Learning:

5. Linear regression

6. Linear two-class classification

7. Linear multi-class classification

8. Linear unsupervised learning

9. Feature engineering and selection

Part III. Nonlinear Learning:

10. Principles of nonlinear feature engineering

11. Principles of feature learning

12. Kernel methods

13. Fully-connected neural networks

14. Tree-based learners

Part IV. Appendices: Appendix A. Advanced first and second order optimization methods

Appendix B. Derivatives and automatic differentiation

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