Model Based Machine Learning
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Model Based Machine Learning
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Author : John Michael Winn
language : en
Publisher: Chapman & Hall/CRC
Release Date : 2019-06
Model Based Machine Learning written by John Michael Winn and has been published by Chapman & Hall/CRC this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-06 with categories.
This book is unusual for a machine learning text book in that the authors do not review dozens of different algorithms. Instead they introduce all of the key ideas through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter therefore introduces one case study which is drawn from a real-world application that has been solved using a model-based approach.
Model Based Machine Learning
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Author : John Winn
language : en
Publisher: CRC Press
Release Date : 2023-11-30
Model Based Machine Learning written by John Winn and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-11-30 with Business & Economics categories.
Today, machine learning is being applied to a growing variety of problems in a bewildering variety of domains. A fundamental challenge when using machine learning is connecting the abstract mathematics of a machine learning technique to a concrete, real world problem. This book tackles this challenge through model-based machine learning which focuses on understanding the assumptions encoded in a machine learning system and their corresponding impact on the behaviour of the system. The key ideas of model-based machine learning are introduced through a series of case studies involving real-world applications. Case studies play a central role because it is only in the context of applications that it makes sense to discuss modelling assumptions. Each chapter introduces one case study and works through step-by-step to solve it using a model-based approach. The aim is not just to explain machine learning methods, but also showcase how to create, debug, and evolve them to solve a problem. Features: Explores the assumptions being made by machine learning systems and the effect these assumptions have when the system is applied to concrete problems Explains machine learning concepts as they arise in real-world case studies Shows how to diagnose, understand and address problems with machine learning systems Full source code available, allowing models and results to be reproduced and explored Includes optional deep-dive sections with more mathematical details on inference algorithms for the interested reader Jo
Model Based Reinforcement Learning
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Author : Milad Farsi
language : en
Publisher: John Wiley & Sons
Release Date : 2023-01-05
Model Based Reinforcement Learning written by Milad Farsi and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-05 with Science categories.
Model-Based Reinforcement Learning Explore a comprehensive and practical approach to reinforcement learning Reinforcement learning is an essential paradigm of machine learning, wherein an intelligent agent performs actions that ensure optimal behavior from devices. While this paradigm of machine learning has gained tremendous success and popularity in recent years, previous scholarship has focused either on theory—optimal control and dynamic programming – or on algorithms—most of which are simulation-based. Model-Based Reinforcement Learning provides a model-based framework to bridge these two aspects, thereby creating a holistic treatment of the topic of model-based online learning control. In doing so, the authors seek to develop a model-based framework for data-driven control that bridges the topics of systems identification from data, model-based reinforcement learning, and optimal control, as well as the applications of each. This new technique for assessing classical results will allow for a more efficient reinforcement learning system. At its heart, this book is focused on providing an end-to-end framework—from design to application—of a more tractable model-based reinforcement learning technique. Model-Based Reinforcement Learning readers will also find: A useful textbook to use in graduate courses on data-driven and learning-based control that emphasizes modeling and control of dynamical systems from data Detailed comparisons of the impact of different techniques, such as basic linear quadratic controller, learning-based model predictive control, model-free reinforcement learning, and structured online learning Applications and case studies on ground vehicles with nonholonomic dynamics and another on quadrator helicopters An online, Python-based toolbox that accompanies the contents covered in the book, as well as the necessary code and data Model-Based Reinforcement Learning is a useful reference for senior undergraduate students, graduate students, research assistants, professors, process control engineers, and roboticists.
Model Based Reinforcement Learning
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Author : Thomas M. Moerland
language : en
Publisher:
Release Date : 2023-01-04
Model Based Reinforcement Learning written by Thomas M. Moerland and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023-01-04 with categories.
Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This monograph surveys an integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps: dynamics model learning and planning-learning integration. In this comprehensive survey of the topic, the authors first cover dynamics model learning, including challenges such as dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. They then present a systematic categorization of planning-learning integration, including aspects such as: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop. In conclusion the authors discuss implicit model-based RL as an end-to-end alternative for model learning and planning, and cover the potential benefits of model-based RL. Along the way, the authors draw connections to several related RL fields, including hierarchical RL and transfer learning. This monograph contains a broad conceptual overview of the combination of planning and learning for Markov Decision Process optimization. It provides a clear and complete introduction to the topic for students and researchers alike.
Model Based Clustering And Classification For Data Science
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Author : Charles Bouveyron
language : en
Publisher: Cambridge University Press
Release Date : 2019-07-25
Model Based Clustering And Classification For Data Science written by Charles Bouveyron and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-07-25 with Business & Economics categories.
Colorful example-rich introduction to the state-of-the-art for students in data science, as well as researchers and practitioners.
Interpretable Machine Learning
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Author : Christoph Molnar
language : en
Publisher: Lulu.com
Release Date : 2020
Interpretable Machine Learning written by Christoph Molnar and has been published by Lulu.com this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020 with Artificial intelligence categories.
This book is about making machine learning models and their decisions interpretable. After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME. All interpretation methods are explained in depth and discussed critically. How do they work under the hood? What are their strengths and weaknesses? How can their outputs be interpreted? This book will enable you to select and correctly apply the interpretation method that is most suitable for your machine learning project.
Model Based Reinforcement Learning
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Author : THOMAS M. MOERLAND; JOOST BROEKENS; ASKE PLAAT; CA.
language : en
Publisher:
Release Date : 2023
Model Based Reinforcement Learning written by THOMAS M. MOERLAND; JOOST BROEKENS; ASKE PLAAT; CA. and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2023 with COMPUTERS categories.
Sequential decision making, commonly formalized as Markov Decision Process (MDP) optimization, is an important challenge in artificial intelligence. Two key approaches to this problem are reinforcement learning (RL) and planning. This monograph surveys an integration of both fields, better known as model-based reinforcement learning. Model-based RL has two main steps: dynamics model learning and planning-learning integration. In this comprehensive survey of the topic, the authors first cover dynamics model learning, including challenges such as dealing with stochasticity, uncertainty, partial observability, and temporal abstraction. They then present a systematic categorization of planning-learning integration, including aspects such as: where to start planning, what budgets to allocate to planning and real data collection, how to plan, and how to integrate planning in the learning and acting loop.In conclusion the authors discuss implicit model-based RL as an end-to-end alternative for model learning and planning, and cover the potential benefits of model-based RL. Along the way, the authors draw connections to several related RL fields, including hierarchical RL and transfer learning. This monograph contains a broad conceptual overview of the combination of planning and learning for Markov Decision Process optimization. It provides a clear and complete introduction to the topic for students and researchers alike.
Machine Learning
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Author : Kamal Kant Hiran
language : en
Publisher: BPB Publications
Release Date : 2021-09-16
Machine Learning written by Kamal Kant Hiran and has been published by BPB Publications this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-09-16 with Computers categories.
Concepts of Machine Learning with Practical Approaches. KEY FEATURES ● Includes real-scenario examples to explain the working of Machine Learning algorithms. ● Includes graphical and statistical representation to simplify modeling Machine Learning and Neural Networks. ● Full of Python codes, numerous exercises, and model question papers for data science students. DESCRIPTION The book offers the readers the fundamental concepts of Machine Learning techniques in a user-friendly language. The book aims to give in-depth knowledge of the different Machine Learning (ML) algorithms and the practical implementation of the various ML approaches. This book covers different Supervised Machine Learning algorithms such as Linear Regression Model, Naïve Bayes classifier Decision Tree, K-nearest neighbor, Logistic Regression, Support Vector Machine, Random forest algorithms, Unsupervised Machine Learning algorithms such as k-means clustering, Hierarchical Clustering, Probabilistic clustering, Association rule mining, Apriori Algorithm, f-p growth algorithm, Gaussian mixture model and Reinforcement Learning algorithm such as Markov Decision Process (MDP), Bellman equations, policy evaluation using Monte Carlo, Policy iteration and Value iteration, Q-Learning, State-Action-Reward-State-Action (SARSA). It also includes various feature extraction and feature selection techniques, the Recommender System, and a brief overview of Deep Learning. By the end of this book, the reader can understand Machine Learning concepts and easily implement various ML algorithms to real-world problems. WHAT YOU WILL LEARN ● Perform feature extraction and feature selection techniques. ● Learn to select the best Machine Learning algorithm for a given problem. ● Get a stronghold in using popular Python libraries like Scikit-learn, pandas, and matplotlib. ● Practice how to implement different types of Machine Learning techniques. ● Learn about Artificial Neural Network along with the Back Propagation Algorithm. ● Make use of various recommended systems with powerful algorithms. WHO THIS BOOK IS FOR This book is designed for data science and analytics students, academicians, and researchers who want to explore the concepts of machine learning and practice the understanding of real cases. Knowing basic statistical and programming concepts would be good, although not mandatory. TABLE OF CONTENTS 1. Introduction 2. Supervised Learning Algorithms 3. Unsupervised Learning 4. Introduction to the Statistical Learning Theory 5. Semi-Supervised Learning and Reinforcement Learning 6. Recommended Systems
Building Successful Business Models Based On Artificial Intelligence
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Author : Bert Langa
language : en
Publisher: Innovation
Release Date : 2018-05-05
Building Successful Business Models Based On Artificial Intelligence written by Bert Langa and has been published by Innovation this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-05-05 with Business & Economics categories.
Every few years, there is a technological trend that leads to the creation of thousands of startups and/or new businesses. At present, we can say without any doubt that one of these trends is Machine Learning (Artificial Intelligence).There is a very powerful reason that this is happening. Currently, we are transitioning from the industrial economy born in the late nineteenth century to a new digital economy centered on data. In this data economy, the success of an organization depends to a large extent on how it uses data to make better decisions. Therefore, leading companies are starting to use their data and Machine Learning algorithms to improve their business processes and, consequently, their results.To put it in context, McKinsey (one of the leading Management Consulting companies worldwide) tells us that "Tech giants including Baidu and Google are spending between $20B to $30B on AI, with 90% of this spent on R&D and deployment, and 10% on AI acquisitions".Amazing, right? Can you imagine capturing one-thousandth of these investments with a new startup or a new business model? Well, that is the main objective of this course: explaining the key concepts of Machine Learning in a very practical way, along with the methods needed for creating disruptive Business Models based on said Tech Trend.That way, you can take advantage of this tremendous opportunity and become a successful businessperson or entrepreneur.
Variational Methods For Machine Learning With Applications To Deep Networks
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Author : Lucas Pinheiro Cinelli
language : en
Publisher: Springer Nature
Release Date : 2021-05-10
Variational Methods For Machine Learning With Applications To Deep Networks written by Lucas Pinheiro Cinelli and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-05-10 with Technology & Engineering categories.
This book provides a straightforward look at the concepts, algorithms and advantages of Bayesian Deep Learning and Deep Generative Models. Starting from the model-based approach to Machine Learning, the authors motivate Probabilistic Graphical Models and show how Bayesian inference naturally lends itself to this framework. The authors present detailed explanations of the main modern algorithms on variational approximations for Bayesian inference in neural networks. Each algorithm of this selected set develops a distinct aspect of the theory. The book builds from the ground-up well-known deep generative models, such as Variational Autoencoder and subsequent theoretical developments. By also exposing the main issues of the algorithms together with different methods to mitigate such issues, the book supplies the necessary knowledge on generative models for the reader to handle a wide range of data types: sequential or not, continuous or not, labelled or not. The book is self-contained, promptly covering all necessary theory so that the reader does not have to search for additional information elsewhere. Offers a concise self-contained resource, covering the basic concepts to the algorithms for Bayesian Deep Learning; Presents Statistical Inference concepts, offering a set of elucidative examples, practical aspects, and pseudo-codes; Every chapter includes hands-on examples and exercises and a website features lecture slides, additional examples, and other support material.