| 000 | 00000cam u2200205 a 4500 | |
| 001 | 000046097368 | |
| 005 | 20250820102604 | |
| 008 | 211104s2020 enka b 001 0 eng | |
| 010 | ▼a 2019040762 | |
| 015 | ▼a GBC022925 ▼2 bnb | |
| 020 | ▼a 9781108470049 (hardcover) | |
| 020 | ▼a 1108470041 (hardcover) | |
| 020 | ▼a 9781108455145 (paperback) | |
| 020 | ▼a 110845514X (paperback) | |
| 020 | ▼a 9781108679930 (electronic publication) | |
| 035 | ▼a (KERIS)REF000019428058 | |
| 040 | ▼a LBSOR/DLC ▼b eng ▼e rda ▼c DLC ▼d OCLCO ▼d UKMGB ▼d YDX ▼d 211009 | |
| 042 | ▼a pcc | |
| 050 | 0 0 | ▼a Q325.5 ▼b .D45 2020 |
| 082 | 0 0 | ▼a 006.3/1 ▼2 23 |
| 084 | ▼a 006.31 ▼2 DDCK | |
| 090 | ▼a 006.31 ▼b D325m | |
| 100 | 1 | ▼a Deisenroth, Marc Peter, ▼e author ▼0 AUTH(211009)172546. |
| 245 | 1 0 | ▼a Mathematics for machine learning / ▼c Marc Peter Deisenroth, A. Aldo Faisal, Cheng Soon Ong. |
| 260 | ▼a Cambridge, UK ; ▼a New York, NY : ▼b Cambridge University Press, ▼c 2020. | |
| 264 | 1 | ▼a Cambridge, United Kingdom ; ▼a New York, NY : ▼b Cambridge University Press, ▼c 2020. |
| 300 | ▼a xvii, 371 p. : ▼b ill. (some col.) ; ▼c 26 cm. | |
| 336 | ▼a text ▼b txt ▼2 rdacontent | |
| 337 | ▼a unmediated ▼b n ▼2 rdamedia | |
| 338 | ▼a volume ▼b nc ▼2 rdacarrier | |
| 504 | ▼a Includes bibliographical references (p. 357-366) and index. | |
| 505 | 0 | ▼a Introduction and motivation -- Linear algebra -- Analytic geometry -- Matrix decompositions -- Vector calculus -- Probability and distribution -- Continuous optimization -- When models meet data -- Linear regression -- Dimensionality reduction with principal component analysis -- Density estimation with Gaussian mixture models -- Classification with support vector machines. |
| 520 | ▼a "The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability, and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models, and support vector machines. For students and others with a mathematical background, these derivations provide a starting point to machine learning texts. For those learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts"-- ▼c Provided by publisher. | |
| 650 | 0 | ▼a Machine learning ▼x Mathematics. |
| 700 | 1 | ▼a Faisal, A. Aldo, ▼e author ▼0 AUTH(211009)172547. |
| 700 | 1 | ▼a Ong, Cheng Soon, ▼e author ▼0 AUTH(211009)172548. |
| 945 | ▼a KLPA |
소장정보
| No. | 소장처 | 청구기호 | 등록번호 | 도서상태 | 반납예정일 | 예약 | 서비스 |
|---|---|---|---|---|---|---|---|
| No. 1 | 소장처 과학도서관/Sci-Info/지정도서 | 청구기호 006.31 D325m | 등록번호 121258371 (14회 대출) | 도서상태 지정도서 | 반납예정일 | 예약 | 서비스 |
컨텐츠정보
책소개
The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or computer science students, or professionals, to efficiently learn the mathematics. This self-contained textbook bridges the gap between mathematical and machine learning texts, introducing the mathematical concepts with a minimum of prerequisites. It uses these concepts to derive four central machine learning methods: linear regression, principal component analysis, Gaussian mixture models and support vector machines. For students?and others?with a mathematical background, these derivations provide a starting point to machine learning texts. For?those?learning the mathematics for the first time, the methods help build intuition and practical experience with applying mathematical concepts. Every chapter includes worked examples and exercises to test understanding. Programming tutorials are offered on the book's web site.
Distills key concepts from linear algebra, geometry, matrices, calculus, optimization, probability and statistics that are used in machine learning.
정보제공 :
목차
1. Introduction and motivation; 2. Linear algebra; 3. Analytic geometry; 4. Matrix decompositions; 5. Vector calculus; 6. Probability and distribution; 7. Optimization; 8. When models meet data; 9. Linear regression; 10. Dimensionality reduction with principal component analysis; 11. Density estimation with Gaussian mixture models; 12. Classification with support vector machines.