Introduction to algorithms for data mining and machine learning

✍ Scribed by Yang, Xin-She

• Publisher: Academic Press; Elsevier
• Year: 2019
• Tongue: English
• Leaves: 180
• Category: Library

✦ Synopsis

Introduction to Algorithms for Data Mining and Machine Learning introduces the essential ideas behind all key algorithms and techniques for data mining and machine learning, along with optimization techniques. Its strong formal mathematical approach, well selected examples, and practical software recommendations help readers develop confidence in their data modeling skills so they can process and interpret data for classification, clustering, curve-fitting and predictions. Masterfully balancing theory and practice, it is especially useful for those who need relevant, well explained, but not rigorous (proofs based) background theory and clear guidelines for working with big data;Introduction to optimization -- Mathematical foundations -- Optimization algorithms -- Data fitting and regression -- Logistic regression, PCA, LDA, and ICA -- Data mining techniques -- Support vector machine and regression -- Neural networks and deep learning

✦ Table of Contents

Cover......Page 1
Introduction toAlgorithms for DataMining and MachineLearning......Page 4
Copyright......Page 5
About the author......Page 6
Preface......Page 7
Acknowledgments......Page 9
1.1.1 Essence of an algorithm......Page 10
1.1.3 Types of algorithms......Page 12
1.2.1 A simple example......Page 13
1.2.2 General formulation of optimization......Page 16
1.2.3 Feasible solution......Page 18
1.3 Unconstrained optimization......Page 19
1.3.1 Univariate functions......Page 20
1.3.2 Multivariate functions......Page 21
1.4 Nonlinear constrained optimization......Page 23
1.4.1 Penalty method......Page 24
1.4.2 Lagrange multipliers......Page 25
1.4.3 Karush-Kuhn-Tucker conditions......Page 26
1.5 Notes on software......Page 27
2 Mathematical foundations......Page 28
2.1.1 Linear and affine functions......Page 29
2.1.2 Convex functions......Page 30
2.2 Computational complexity......Page 31
2.2.1 Time and space complexity......Page 33
2.2.2 Complexity of algorithms......Page 34
2.3.1 Norms......Page 35
2.3.2 Regularization......Page 37
2.4.1 Random variables......Page 38
2.4.2 Probability distributions......Page 39
2.4.3 Conditional probability and Bayesian rule......Page 41
2.4.4 Gaussian process......Page 43
2.5 Bayesian network and Markov models......Page 44
2.6 Monte Carlo sampling......Page 45
2.6.2 Metropolis-Hastings algorithm......Page 46
2.7.1 Entropy and cross entropy......Page 48
2.7.2 DL divergence......Page 49
2.8 Fuzzy rules......Page 50
2.10 Notes on software......Page 51
3.1.1 Newton's method......Page 53
3.1.2 Newton's method for multivariate functions......Page 55
3.1.3 Line search......Page 56
3.2 Variants of gradient-based methods......Page 57
3.2.1 Stochastic gradient descent......Page 58
3.2.2 Subgradient method......Page 59
3.2.3 Conjugate gradient method......Page 60
3.3 Optimizers in deep learning......Page 61
3.4 Gradient-free methods......Page 64
3.5.1 Genetic algorithm......Page 66
3.5.2 Differential evolution......Page 68
3.5.4 Bat algorithm......Page 69
3.5.6 Cuckoo search......Page 70
3.5.7 Flower pollination algorithm......Page 71
3.6 Notes on software......Page 72
4.1 Sample mean and variance......Page 74
4.2.1 Maximum likelihood......Page 76
4.2.2 Liner regression......Page 77
4.2.3 Linearization......Page 82
4.2.4 Generalized linear regression......Page 84
4.2.5 Goodness of fit......Page 87
4.3 Nonlinear least squares......Page 88
4.3.1 Gauss-Newton algorithm......Page 89
4.3.3 Weighted least squares......Page 92
4.4 Overfitting and information criteria......Page 93
4.5 Regularization and Lasso method......Page 95
4.6 Notes on software......Page 97
5.1 Logistic regression......Page 98
5.3 Principal component analysis......Page 103
5.4 Linear discriminant analysis......Page 108
5.5 Singular value decomposition......Page 111
5.6 Independent component analysis......Page 112
5.7 Notes on software......Page 115
6 Data mining techniques......Page 116
6.1.2 Distance metric......Page 117
6.2 Hierarchy clustering......Page 118
6.3 k-Nearest-neighbor algorithm......Page 119
6.4 k-Means algorithm......Page 120
6.5.1 Decision tree algorithm......Page 122
6.5.2 ID3 algorithm and C4.5 classifier......Page 123
6.5.3 Random forest......Page 127
6.6.1 Naive Bayesian classifier......Page 128
6.6.2 Bayesian networks......Page 130
6.7.1 Characteristics of big data......Page 131
6.7.3 Mining big data......Page 132
6.8 Notes on software......Page 134
7.1 Statistical learning theory......Page 136
7.2 Linear support vector machine......Page 137
7.3 Kernel functions and nonlinear SVM......Page 140
7.4 Support vector regression......Page 142
7.5 Notes on software......Page 144
8.1 Learning......Page 146
8.2.1 Neuron models......Page 147
8.2.2 Activation models......Page 148
8.2.3 Artificial neural networks......Page 150
8.3 Back propagation algorithm......Page 153
8.4 Loss functions in ANN......Page 154
8.6 Network architecture......Page 156
8.7.1 Convolutional neural networks......Page 158
8.7.1.1 Convolution and activation......Page 159
8.7.1.2 Pooling......Page 161
8.7.1.3 Flattening......Page 162
8.7.1.4 Fully connected neural network......Page 163
8.7.2 Restricted Boltzmann machine......Page 164
8.7.3 Deep neural nets......Page 165
8.7.4 Trends in deep learning......Page 166
8.8 Tuning of hyperparameters......Page 167
8.9 Notes on software......Page 168
Bibliography......Page 169
Index......Page 177
Back Cover......Page 180

✦ Subjects

COMPUTERS--General;Data mining;Electronic books;Machine learning;COMPUTERS -- General