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· 분류 : 외국도서 > 컴퓨터 > 컴퓨터 일반
· ISBN : 9780262046824
· 쪽수 : 864쪽
· 출판일 : 2022-03-01
목차
1 Introduction 1
I Foundations 29
2 Probability: Univariate Models 31
3 Probability: Multivariate Models 75
4 statistics 103
5 Decision Theory 163
6 Information Theory 199
7 Linear Algebra 221
8 Optimization 269
II Linear Models 315
9 Linear Discriminant Analysis 317
10 Logistic Regression 333
11 Linear Regression 365
12 Generalized Linear Models * 409
III Deep Neural Networks 417
13 Neural Networks for Structured Data 419
14 Neural Networks for Images 461
15 Neural Networks for Sequences 497
IV Nonparametric Models 539
16 Exemplar-based Methods 541
17 Kernel Methods * 561
18 Trees, Forests, Bagging, and Boosting 597
V Beyond Supervised Learning 619
19 Learning with Fewer Labeled Examples 621
20 Dimensionality Reduction 651
21 Clustering 709
22 Recommender Systems 735
23 Graph Embeddings * 747
A Notation 767
