Machine Learning Control by Symbolic Regression
- Length: 164 pages
- Edition: 1
- Language: English
- Publisher: Springer
- Publication Date: 2021-11-22
- ISBN-10: 3030832120
- ISBN-13: 9783030832124
- Sales Rank: #0 (See Top 100 Books)
This book provides comprehensive coverage on a new direction in computational mathematics research: automatic search for formulas. Formulas must be sought in all areas of science and life: these are the laws of the universe, the macro and micro world, fundamental physics, engineering, weather and natural disasters forecasting; the search for new laws in economics, politics, sociology. Accumulating many years of experience in the development and application of numerical methods of symbolic regression to solving control problems, the authors offer new possibilities not only in the field of control automation, but also in the design of completely different optimal structures in many fields.
For specialists in the field of control, Machine Learning Control by Symbolic Regression opens up a new promising direction of research and acquaints scientists with the methods of automatic construction of control systems.
For specialists in the field of machine learning, the book opens up a new, much broader direction than neural networks: methods of symbolic regression. This book makes it easy to master this new area in machine learning and apply this approach everywhere neural networks are used. For mathematicians, the book opens up a new approach to the construction of numerical methods for obtaining analytical solutions to unsolvable problems; for example, numerical analytical solutions of algebraic equations, differential equations, non-trivial integrals, etc.
For specialists in the field of artificial intelligence, the book offers a machine way to solve problems, framed in the form of analytical relationships.
Introduction Chapter 1: Machine Learning: A Brief History Donald Hebb - The Organization of Behavior Samuel Arthur - Neural Networks, Checkers and Rote Learning Rosenblatt’s Perceptron Marcello Pelillo - The Nearest Neighbor Algorithm Perceptrons and Multilayers Going Separate Ways Robert Schapire - The Strength of Weak Learnability Advancing into Speech and Facial Recognition Present Day Machine Learning Chapter 2: Fundamentals of Python for Machine Learning What is Python? Why Python? Other Programming Languages Effective Implementation of Machine Learning Algorithms Mastering Machine Learning with Python Chapter 3: Data Analysis in Python Importance of Learning Data Analysis in Python Building Predictive Models in Python Python Data Structures Python Libraries for Data Analysis Chapter 4: Comparing Deep Learning and Machine Learning Deep Learning vs Machine Learning Problem Solving Approaches Different Use Cases Chapter 5: Machine Learning with Scikit-Learn Representing Data in Scikit-Learn Features Matrix Target Arrays Estimator API Supervised Learning in Scikit-Learn Unsupervised Learning in Scikit-Learn Chapter 6: Deep Learning with TensorFlow Brief History of TensorFlow The TensorFlow Platform TensorFlow Environments TensorFlow Components Algorithm Support Creating TensorFlow Pipelines Chapter 7: Deep Learning with PyTorch and Keras PyTorch Model Structures Initializing PyTorch Model Parameters Principles Supporting Keras Getting Started Keras Preferences Keras Functional API Chapter 8: Role of Machine Learning in the Internet of Things (IoT) Fusing Machine Learning and IoT Machine Learning Challenges in IoT Chapter 9: Looking to the Future with Machine Learning The Business Angle AI in the Future Conclusion
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