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| 005 | 20220324094408 | |
| 008 | 220322s2019 caua b 001 0 eng d | |
| 015 | ▼a GBB933013 ▼2 bnb | |
| 020 | ▼a 9781484239155 ▼q (pbk.) | |
| 020 | ▼a 1484239156 ▼q (pbk.) | |
| 020 | ▼z 9781484239162 ▼q ebook | |
| 035 | ▼a (KERIS)REF000019087365 | |
| 040 | ▼a UKMGB ▼b eng ▼e rda ▼c UKMGB ▼d OCLCO ▼d JRZ ▼d BUB ▼d OCLCF ▼d 211009 | |
| 082 | 0 4 | ▼a 006.31 ▼2 23 |
| 084 | ▼a 006.31 ▼2 DDCK | |
| 090 | ▼a 006.31 ▼b P184ma | |
| 100 | 1 | ▼a Paluszek, Michael, ▼e author. |
| 245 | 1 0 | ▼a MATLAB machine learning recipes : ▼b a problem-solution approach / ▼c Michael Paluszek, Stephanie Thomas. |
| 250 | ▼a Second edition. | |
| 260 | ▼a [Berkeley, California] : ▼b Apress, ▼c 2019. | |
| 264 | 1 | ▼a [Berkeley, California] : ▼b Apress, ▼c [2019] |
| 264 | 2 | ▼a New York, NY : Distributed by Springer Science + Business Media, |
| 264 | 4 | ▼c ©2019 |
| 300 | ▼a xix, 347 p. : ▼b ill. (some col.) ; ▼c 26 cm. | |
| 336 | ▼a text ▼2 rdacontent | |
| 337 | ▼a unmediated ▼2 rdamedia | |
| 338 | ▼a volume ▼2 rdacarrier | |
| 504 | ▼a Includes bibliographical references and index. | |
| 505 | 0 | ▼a Introduction -- An overview of machine learning -- Representation of data for machine learning in MATLAB -- MATLAB graphics -- Kalman filters -- Adaptive control -- Fuzzy logic -- Data classification with decision trees -- Introduction to neural nets -- Classification of numbers using neural networks -- Pattern recognition with deep learning -- Neural aircraft control -- Multiple hypothesis testing -- Autonomous driving with multiple hypothesis testing -- Case-based expert systems -- A brief history of autonomous learning -- Software for machine learning. |
| 520 | ▼a Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem. All code in "MATLAB machine learning recipes: a problem-solution approach" is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more. You will: Learn to write code for machine learning, adaptive control and estimation using MATLAB ; See how these three areas complement each other ; Understaand why these three areas are needed for robust machine learning applications ; Use MATLAB graphics and visualization tools for machine learning ; Code real-world examples in MATLAB for major applications of machine learning in big data. | |
| 630 | 0 0 | ▼a MATLAB. |
| 650 | 0 | ▼a Machine learning. |
| 700 | 1 | ▼a Thomas, Stephanie, ▼e author. |
| 945 | ▼a ITMT |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.31 P184ma | 등록번호 121259328 (3회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem.
All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.
What you'll learn:
- How to write code for machine learning, adaptive control and estimation using MATLAB
- How these three areas complement each other
- How these three areas are needed for robust machine learning applications
- How to use MATLAB graphics and visualization tools for machine learning
- How to code real world examples in MATLAB for major applications of machine learning in big data
Who is this book for:
The primary audiences are engineers, data scientists and students wanting a comprehensive and code cookbook rich in examples on machine learning using MATLAB.
New feature
Harness the power of MATLAB to resolve a wide range of machine learning challenges. This book provides a series of examples of technologies critical to machine learning. Each example solves a real-world problem.
All code in MATLAB Machine Learning Recipes: A Problem-Solution Approach is executable. The toolbox that the code uses provides a complete set of functions needed to implement all aspects of machine learning. Authors Michael Paluszek and Stephanie Thomas show how all of these technologies allow the reader to build sophisticated applications to solve problems with pattern recognition, autonomous driving, expert systems, and much more.
You will:
- Learn to write code for machine learning, adaptive control and estimation using MATLAB
- See how these three areas complement each other
- Understand why these three areas are needed for robust machine learning applications
- Use MATLAB graphics and visualization tools for machine learning
- Code real world examples in MATLAB for major applications of machine learning in big data
정보제공 :
목차
1 Overview 2 Data Representation 3 MATLAB Graphics 4 Kalman Filters 5 Adaptive Control 6 Fuzzy Logic 7 Data Classification with Decision Trees 8 Simple Neural Nets 9 Classification with Neural Nets 10 Neural Nets with Deep Learning 11 Neural Aircraft Control 12 Multiple Hypothesis Testing 13 Autonomous Driving with MHT 14 Case-Based Expert Systems Appendix A: A Brief History of Autonomous Learning Appendix B: Software for Machine Learning