| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000045947766 | |
| 005 | 20200123130805 | |
| 008 | 180718s2017 caua b 001 0 eng d | |
| 010 | ▼a 2016963347 | |
| 020 | ▼a 9781484222492 (pbk.) | |
| 020 | ▼a 1484222490 (pbk.) | |
| 035 | ▼a (KERIS)REF000018590528 | |
| 040 | ▼a YDX ▼b eng ▼c YDX ▼e rda ▼d TXA ▼d OCLCF ▼d TOH ▼d BDX ▼d DLC ▼d 211009 | |
| 050 | 0 0 | ▼a Q325.5 ▼b .P35 2017 |
| 082 | 0 4 | ▼a 006.31 ▼2 23 |
| 084 | ▼a 006.31 ▼2 DDCK | |
| 090 | ▼a 006.31 ▼b P184m | |
| 100 | 1 | ▼a Paluszek, Michael. |
| 245 | 1 0 | ▼a MATLAB machine learning / ▼c Michael Paluszek, Stephanie Thomas. |
| 260 | ▼a [Berkeley, CA?] : ▼b Apress ; ▼a New York, NY : ▼b Distributed to the book trade worldwide by Springer Science+Business Media, ▼c c2017. | |
| 300 | ▼a xix, 326 p. ; : ▼b ill. ; ▼c 26 cm. | |
| 504 | ▼a Includes bibliographical references and index. | |
| 630 | 0 0 | ▼a MATLAB. |
| 650 | 0 | ▼a Machine learning. |
| 700 | 1 | ▼a Thomas, Stephanie. |
| 776 | 0 8 | ▼i Online version: ▼a Paluszek, Michael. ▼t MATLAB machine learning ▼z 9781484222508 ▼w (211009) 000046012095 |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info(2층서고)/ | 청구기호 006.31 P184m | 등록번호 121245293 (9회 대출) | 도서상태 대출가능 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
This book is a comprehensive guide to machine learning with worked examples in MATLAB. It?starts with an overview of the history of Artificial Intelligence and automatic control and how the field of machine learning grew from these. It provides descriptions of all major areas in machine learning.?
The book reviews commercially available packages for machine learning and shows how they fit into the field. The book then shows how MATLAB can be used to solve machine learning problems and how MATLAB graphics can enhance the programmer’s understanding of the results and help users of their software grasp the results.?
Machine Learning can be very mathematical. The mathematics for each area is introduced in a clear and concise form so that even casual readers can understand the math. Readers from all areas of engineering will see connections to what they know and will learn new technology.?
What you'll learn:
- An overview of the field of machine learning
- Commercial and open source packages in MATLAB
- How to use MATLAB for programming and building machine learning applications
- MATLAB graphics for machine learning
- Practical real world examples in MATLAB for major applications of machine learning in big data
Who is this book for:
<The primary audiences are engineers and engineering students wanting a comprehensive and practical introduction to machine learning.
정보제공 :
목차
1 Overview of Machine Learning 2 The History of Machine Learning 3 Software for machine learning 4 Representation of data for Machine Learning in MATLAB 5 MATLAB Graphics 6 Machine Learning Examples in MATLAB 7 Face Recognition with Deep Learning 8 Data Classification 9 Classification of Numbers Using Neural Networks 10 Kalman Filters 11 Adaptive Control 12 Autonomous Driving Bibliography.