Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to learning methods. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering.
Written in an engaging and rigorous style by a world authority in the field, this is an accessible and comprehensive introduction to learning methods. With downloadable Matlab code and solutions for instructors, this is the ideal introduction for students of data science, machine learning and engineering.
Ali H. Sayed is Professor and Dean of Engineering at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He has also served as Distinguished Professor and Chairman of Electrical Engineering at the University of California, Los Angeles, USA, and as President of the IEEE Signal Processing Society. He is a member of the US National Academy of Engineering (NAE) and The World Academy of Sciences (TWAS), and a recipient of the 2022 IEEE Fourier Award and the 2020 IEEE Norbert Wiener Society Award. He is a Fellow of the IEEE.
Inhaltsangabe
Preface Notation 50. Least-squares problems 51. Regularization 52. Nearest-neighbor rule 53. Self-organizing maps 54. Decision trees 55. Naive Bayes classifier 56. Linear discriminant analysis 57. Principal component analysis 58. Dictionary learning 59. Logistic regression 60. Perceptron 61. Support vector machines 62. Bagging and boosting 63. Kernel methods 64. Generalization theory 65. Feedforward neural networks 66. Deep belief networks 67. Convolutional networks 68. Generative networks 69. Recurrent networks 70. Explainable learning 71. Adversarial attacks 72. Meta learning Author index Subject index.