Atjaunināt sīkdatņu piekrišanu

Fundamentals of Machine Learning [Mīkstie vāki]

(Professor of Computer Science, Dalhousie University)
  • Formāts: Paperback / softback, 260 pages, height x width x depth: 245x189x14 mm, weight: 568 g
  • Izdošanas datums: 28-Nov-2019
  • Izdevniecība: Oxford University Press
  • ISBN-10: 0198828047
  • ISBN-13: 9780198828044
Citas grāmatas par šo tēmu:
  • Mīkstie vāki
  • Cena: 56,65 €
  • Grāmatu piegādes laiks ir 3-4 nedēļas, ja grāmata ir uz vietas izdevniecības noliktavā. Ja izdevējam nepieciešams publicēt jaunu tirāžu, grāmatas piegāde var aizkavēties.
  • Daudzums:
  • Ielikt grozā
  • Piegādes laiks - 4-6 nedēļas
  • Pievienot vēlmju sarakstam
Fundamentals of Machine LearningPalielināt
  • Formāts: Paperback / softback, 260 pages, height x width x depth: 245x189x14 mm, weight: 568 g
  • Izdošanas datums: 28-Nov-2019
  • Izdevniecība: Oxford University Press
  • ISBN-10: 0198828047
  • ISBN-13: 9780198828044
Citas grāmatas par šo tēmu:
Permanent link: https://www.kriso.lv/db/9780198828044.html
Keywords:
Interest in machine learning is exploding worldwide, both in research and for industrial applications. Machine learning is fast becoming a fundamental part of everyday life.

This book is a brief introduction to this area - exploring its importance in a range of many disciplines, from science to engineering, and even its broader impact on our society. The book is written in a style that strikes a balance between brevity of explanation, rigorous mathematical argument, and outlines principle ideas. At the same time, it provides a comprehensive overview of a variety of methods and their application within this field. This includes an introduction to Bayesian approaches to modeling, as well as deep learning.

Writing small programs to apply machine learning techniques is made easy by high level programming systems, and this book shows examples in Python with the machine learning libraries 'sklearn' and 'Keras'. The first four chapters concentrate on the practical side of applying machine learning techniques. The following four chapters discuss more fundamental concepts that includes their formulation in a probabilistic context. This is followed by two more chapters on advanced models, that of recurrent neural networks and that of reinforcement learning. The book closes with a brief discussion on the impact of machine learning and AI on our society.

Fundamentals of Machine Learning provides a brief and accessible introduction to this rapidly growing field, one that will appeal to students and researchers across computer science and computational neuroscience, as well as the broader cognitive sciences.
1 Introduction
1(16)
1.1 The basic idea and history of machine learning
1(4)
1.2 Mathematical formulation of the basic learning problem
5(4)
1.3 Non-linear regression in high-dimensions
9(3)
1.4 Recent advances
12(1)
1.5 No free lunch, but worth the bite
13(4)
I A PRACTICAL GUIDE TO MACHINE LEARNING
2 Scientific programming with Python
17(21)
2.1 Programming environment
17(2)
2.2 Basic language elements
19(7)
2.3 Code efficiency and vectorization
26(2)
2.4 Data handling
28(4)
2.5 Image processing and convolutional filters
32(6)
3 Machine learning with sklearn
38(28)
3.1 Classification with support vector machines, random forests, and multilayer perceptrons
39(2)
3.2 Performance measures and evaluations
41(3)
3.3 Data handling
44(5)
3.4 Dimensionality reduction, feature selection, and t-SNE
49(3)
3.5 Decision trees and random forests
52(3)
3.6 Support vector machines (SVM)
55(11)
4 Neural networks and Keras
66(27)
4.1 Neurons and the threshold perceptron
66(2)
4.2 Multilayer perceptron (MLP) and Keras
68(5)
4.3 Representational learning
73(3)
4.4 Convolutional neural Networks (CNNs)
76(9)
4.5 What and where
85(1)
4.6 More tricks of the trade
86(7)
II Foundations Regression And Probabilistic Modeling
5 Regression and optimization
93(28)
5.1 Linear regression and gradient descent
93(3)
5.2 Error surface and challenges for gradient descent
96(2)
5.3 Advanced gradient optimization (learning)
98(3)
5.4 Regularization: ridge regression and LASSO
101(4)
5.5 Non-linear regression
105(2)
5.6 Backpropagation
107(10)
5.7 Automatic differentiation
117(4)
6 Basic probability theory
121(20)
6.1 Random numbers and their probability (density) function
121(3)
6.2 Moments: mean, variance, etc.
124(3)
6.3 Examples of probability (density) functions
127(2)
6.4 Some advanced concepts
129(3)
6.5 Density functions of multiple random variables
132(2)
6.6 How to combine prior knowledge with new evidence
134(7)
7 Probabilistic regression and Bayes nets
141(21)
7.1 Probabilistic models
141(3)
7.2 Learning in probabilistic models: Maximum likelihood estimate
144(3)
7.3 Probabilistic classification
147(3)
7.4 Maximum a posteriori (MAP) and regularization with priors
150(3)
7.5 Bayes nets: multivariate causal modeling
153(4)
7.6 Probabilistic and stochastic neural networks
157(5)
8 Generative models
162(21)
8.1 Modeling classes
162(1)
8.2 Supervised generative models
163(4)
8.3 Naive Bayes
167(3)
8.4 Self-supervised generative models
170(4)
8.5 Generative neural networks
174(9)
III ADVANCED LEARNING MODELS
9 Cyclic models and recurrent neural networks
183(23)
9.1 Sequence processing
184(3)
9.2 Basic sequence processing with multilayer perceptrons and recurrent neural networks in Keras
187(3)
9.3 Gated recurrent neural networks, natural language processing, and attention
190(5)
9.4 Models with symmetric lateral connections
195(11)
10 Reinforcement learning
206(27)
10.1 Formalization of the problem setting
206(5)
10.2 Model-based reinforcement learning
211(6)
10.3 Model-free reinforcement learning
217(5)
10.4 Deep reinforcement learning
222(5)
10.5 Actors and actor-critics
227(3)
10.6 Reinforcement learning in the brain
230(3)
11 Artificial intelligence, the brain, and our society
233(10)
11.1 Different levels of modeling and the brain
233(3)
11.2 Machine learning and artificial intelligence
236(2)
11.3 The impact of machine learning technology on society
238(5)
Index 243
Dr. Trappenberg is a professor of Computer Science at Dalhousie University. He holds a PhD in physics from RWTH Aachen University and held research positions in Canada, Riken Japan, and Oxford England. His main research areas are computational neuroscience, machine learning and robotics. He is the author of Fundamental of Computational Neuroscience and the cofounder of Nexus Robotics and ReelData. He is currently working on applying AI to several other areas in the food industry and in medical applications.