Machine Learning Control - Taming Nonlinear Dynamics and Turbulence (Duriez Thomas)(Pevná vazba)

Machine Learning Control - Taming Nonlinear Dynamics and Turbulence (Duriez Thomas)(Pevná vazba)

This is the first textbook on a generally applicable control strategy for turbulence and other complex nonlinear systems. The approach of the book employs powerful methods of machine learning for optimal nonlinear control laws. This machine learning control (MLC) is motivated and detailed in

Nonlinear Solid Mechanics for Finite Element Analysis: Dynamics (Bonet Javier)(Pevná vazba)

The authors give a unified treatment of nonlinear solid mechanics, dynamic conservation laws, and the associated finite element techniques, alongside exercises, examples, and working code, to provide a self-contained introduction to the dynamic simulation of nonlinear solids suitable for beginning

Hands-On Machine Learning with R (Boehmke Brad)(Pevná vazba)

Hands-on Machine Learning with R provides a practical and applied approach to learning and developing intuition into today's most popular machine learning methods. This book serves as a practitioner's guide to the machine learning process and is meant to help the reader learn to apply the machine

Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent (Zhou Jianlong)(Pevná vazba)

Part I Transparency in Machine Learning.- Part II Visual Explanation of Machine Learning Process.- Part III Algorithmic Explanation of Machine Learning Models.- Part IV User Cognitive Responses in ML-Based Decision Making.- Part V Human and Evaluation of Machine Learning.- Part VI Domain Knowledge

Motor Control and Learning: A Behavioral Emphasis (Schmidt Richard A.)(Pevná vazba)

The Machine Learning: A Probabilistic Perspective (Murphy Kevin P.)(Pevná vazba)

A comprehensive introduction to machine learning that uses probabilistic models and inference as a unifying approach.Today's Web-enabled deluge of electronic data calls for automated methods of data analysis. Machine learning provides these, developing methods that can automatically detect patterns

Bayesian Reasoning and Machine Learning (Barber David)(Pevná vazba)

Machine learning methods extract value from vast data sets quickly and with modest resources. They are established tools in a wide range of industrial applications, including search engines, DNA sequencing, stock market analysis, and robot locomotion, and their use is spreading rapidly. People who

Stability and Control of Nonlinear Time-Varying Systems (Guo Shuli)(Pevná vazba)

Introduction.- Novel Mathematical Modeling and Stability Analysis of Linear Uncertain Systems Subject to Actuator Saturations.- Commuting Matrices, Equilibrium Points for Control Systems with Single Saturated Input.- Stability and Closed Trajectory for 2nd Order Control Systems with Single

Natural Language Processing: A Machine Learning Perspective (Zhang Yue)(Pevná vazba)

With a machine learning approach and less focus on linguistic details, this gentle introduction to natural language processing develops fundamental mathematical and deep learning models for NLP under a unified framework. NLP problems are systematically organised by their machine learning nature,

Aerospace Robotics III (Sasiadek Jerzy)(Pevná vazba)

Parallel Hamiltonian Formulation For Forward Dynamics Of Free-Flying Manipulators.- Nonlinear Model Predictive Control (NMPC) for free-floating space manipulator.- Performance control of a spacecraft-robotic arm system - desired motion tracking.- Detection and decoding of AIS navigation messages by

Dynamics and Control of Mechanical Systems in Offshore Engineering (He Wei)(Paperback)

Preliminaries.- Dynamic Load Positioning.- Coupled Nonlinear Flexible Marine Riser.- Flexible Marine Riser with Vessel Dynamics.- Riser System with a Torque Actuator.- Marine Installation System.- Riser Installation System.- Mooring

Robust Control for Nonlinear Time-Delay Systems (Hua Changchun)(Pevná vazba)

Introduction.- Part I High-order polynomial form.- Robust Stabilization of a Class of Nonlinear Time-delay Systems.- Robust Model Reference Adaptive Control for Interconnected Time-delay Systems.- Part II General Nonlinear Uncertainties.- Decentralized Adaptive Control for Interconnected Systems

Data Mining and Machine Learning: Fundamental Concepts and Algorithms (Zaki Mohammed J.)(Pevná vazba)

The fundamental algorithms in data mining and machine learning form the basis of data science, utilizing automated methods to analyze patterns and models for all kinds of data in applications ranging from scientific discovery to business analytics. This textbook for senior undergraduate and

A First Course in Machine Learning (Rogers Simon)(Pevná vazba)

'A First Course in Machine Learning by Simon Rogers and Mark Girolami is the best introductory book for ML currently available. It combines rigor and precision with accessibility, starts from a detailed explanation of the basic foundations of Bayesian analysis in the simplest of settings, and goes

Foundations of Machine Learning, Second Edition (Mohri Mehryar)(Pevná vazba)

A new edition of a graduate-level machine learning textbook that focuses on the analysis and theory of algorithms.This book is a general introduction to machine learning that can serve as a textbook for graduate students and a reference for researchers. It covers fundamental modern topics in

Transformative Dimensions of Adult Learning (Mezirow Jack)(Pevná vazba)

'Transformative Dimensions of Adult Learning' describes the dynamics of how adults learn--and how their perceptions are transformed by learning--as a framework for formulating educational theory and

Machine Learning Refined: Foundations, Algorithms, and Applications (Watt Jeremy)(Pevná vazba)

With its intuitive yet rigorous approach to machine learning, this text provides students with the fundamental knowledge and practical tools needed to conduct research and build data-driven products. The authors prioritize geometric intuition and algorithmic thinking, and include detail on all the

Prediction, Learning, and Games (Cesa-Bianchi Nicolo)(Pevná vazba)

This important text and reference for researchers and students in machine learning, game theory, statistics and information theory offers a comprehensive treatment of the problem of predicting individual sequences. Unlike standard statistical approaches to forecasting, prediction of individual

Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning (Gilliland Michael)(Pevná vazba)

Discover the role of machine learning and artificial intelligence in business forecasting from some of the brightest minds in the field In Business Forecasting: The Emerging Role of Artificial Intelligence and Machine Learning accomplished authors Michael Gilliland, Len Tashman, and Udo Sglavo

Understanding Process Dynamics and Control (Kravaris Costas)(Pevná vazba)

Presenting a fresh look at process control, this new text demonstrates state-space approach shown in parallel with the traditional approach to explain the strategies used in industry today. Modern time-domain and traditional transform-domain methods are integrated throughout and explain the

Linear and Nonlinear Optimization (Cottle Richard W.)(Pevná vazba)

​This textbook on Linear and Nonlinear Optimization is intended for graduate and advanced undergraduate students in operations research and related fields. It is both literate and mathematically strong, yet requires no prior course in optimization. As suggested by its title, the book is divided

Fundamentals of Vehicle Dynamics, Revised Edition (Gillespie Thomas D.)(Pevná vazba)

A world-recognized expert in the science of vehicle dynamics, Dr. Thomas Gillespie has created an ideal reference book that has been used by engineers for 30 years, ranging from an introduction to the subject at the university level to a common sight on the desks of engineers throughout the

Machine Learning in Asset Pricing (Nagel Stefan)(Pevná vazba)

A groundbreaking, authoritative introduction to how machine learning can be applied to asset pricing Investors in financial markets are faced with an abundance of potentially value-relevant information from a wide variety of different sources. In such data-rich, high-dimensional environments,

Mathematics for Machine Learning (Deisenroth Marc Peter)(Pevná vazba)

The fundamental mathematical tools needed to understand machine learning include linear algebra, analytic geometry, matrix decompositions, vector calculus, optimization, probability and statistics. These topics are traditionally taught in disparate courses, making it hard for data science or