Optimization For Machine Learning

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Optimization for Machine Learning

Suvrit Sra,Sebastian Nowozin,Stephen J. Wright
Optimization for Machine Learning Book PDF

Author : Suvrit Sra,Sebastian Nowozin,Stephen J. Wright
Publisher : MIT Press
Page : 509 pages
File Size : 41,9 Mb
Release : 2012
Category : Computers
ISBN : 9780262016469

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Optimization for Machine Learning by Suvrit Sra,Sebastian Nowozin,Stephen J. Wright Pdf

An up-to-date account of the interplay between optimization and machine learning, accessible to students and researchers in both communities. The interplay between optimization and machine learning is one of the most important developments in modern computational science. Optimization formulations and methods are proving to be vital in designing algorithms to extract essential knowledge from huge volumes of data. Machine learning, however, is not simply a consumer of optimization technology but a rapidly evolving field that is itself generating new optimization ideas. This book captures the state of the art of the interaction between optimization and machine learning in a way that is accessible to researchers in both fields. Optimization approaches have enjoyed prominence in machine learning because of their wide applicability and attractive theoretical properties. The increasing complexity, size, and variety of today's machine learning models call for the reassessment of existing assumptions. This book starts the process of reassessment. It describes the resurgence in novel contexts of established frameworks such as first-order methods, stochastic approximations, convex relaxations, interior-point methods, and proximal methods. It also devotes attention to newer themes such as regularized optimization, robust optimization, gradient and subgradient methods, splitting techniques, and second-order methods. Many of these techniques draw inspiration from other fields, including operations research, theoretical computer science, and subfields of optimization. The book will enrich the ongoing cross-fertilization between the machine learning community and these other fields, and within the broader optimization community.

Accelerated Optimization for Machine Learning

Zhouchen Lin,Huan Li,Cong Fang
Accelerated Optimization for Machine Learning Book PDF

Author : Zhouchen Lin,Huan Li,Cong Fang
Publisher : Springer Nature
Page : 286 pages
File Size : 43,5 Mb
Release : 2020-05-29
Category : Computers
ISBN : 9789811529108

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Accelerated Optimization for Machine Learning by Zhouchen Lin,Huan Li,Cong Fang Pdf

This book on optimization includes forewords by Michael I. Jordan, Zongben Xu and Zhi-Quan Luo. Machine learning relies heavily on optimization to solve problems with its learning models, and first-order optimization algorithms are the mainstream approaches. The acceleration of first-order optimization algorithms is crucial for the efficiency of machine learning. Written by leading experts in the field, this book provides a comprehensive introduction to, and state-of-the-art review of accelerated first-order optimization algorithms for machine learning. It discusses a variety of methods, including deterministic and stochastic algorithms, where the algorithms can be synchronous or asynchronous, for unconstrained and constrained problems, which can be convex or non-convex. Offering a rich blend of ideas, theories and proofs, the book is up-to-date and self-contained. It is an excellent reference resource for users who are seeking faster optimization algorithms, as well as for graduate students and researchers wanting to grasp the frontiers of optimization in machine learning in a short time.

First-order and Stochastic Optimization Methods for Machine Learning

Guanghui Lan
First order and Stochastic Optimization Methods for Machine Learning Book PDF

Author : Guanghui Lan
Publisher : Springer Nature
Page : 591 pages
File Size : 43,7 Mb
Release : 2020-05-15
Category : Mathematics
ISBN : 9783030395681

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First-order and Stochastic Optimization Methods for Machine Learning by Guanghui Lan Pdf

This book covers not only foundational materials but also the most recent progresses made during the past few years on the area of machine learning algorithms. In spite of the intensive research and development in this area, there does not exist a systematic treatment to introduce the fundamental concepts and recent progresses on machine learning algorithms, especially on those based on stochastic optimization methods, randomized algorithms, nonconvex optimization, distributed and online learning, and projection free methods. This book will benefit the broad audience in the area of machine learning, artificial intelligence and mathematical programming community by presenting these recent developments in a tutorial style, starting from the basic building blocks to the most carefully designed and complicated algorithms for machine learning.

Optimization for Machine Learning

Jason Brownlee
Optimization for Machine Learning Book PDF

Author : Jason Brownlee
Publisher : Machine Learning Mastery
Page : 412 pages
File Size : 50,7 Mb
Release : 2021-09-22
Category : Computers
ISBN : 8210379456XXX

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Optimization for Machine Learning by Jason Brownlee Pdf

Optimization happens everywhere. Machine learning is one example of such and gradient descent is probably the most famous algorithm for performing optimization. Optimization means to find the best value of some function or model. That can be the maximum or the minimum according to some metric. Using clear explanations, standard Python libraries, and step-by-step tutorial lessons, you will learn how to find the optimum point to numerical functions confidently using modern optimization algorithms.

Optimization in Machine Learning and Applications

Anand J. Kulkarni,Suresh Chandra Satapathy
Optimization in Machine Learning and Applications Book PDF

Author : Anand J. Kulkarni,Suresh Chandra Satapathy
Publisher : Springer Nature
Page : 202 pages
File Size : 46,6 Mb
Release : 2019-11-29
Category : Technology & Engineering
ISBN : 9789811509940

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Optimization in Machine Learning and Applications by Anand J. Kulkarni,Suresh Chandra Satapathy Pdf

This book discusses one of the major applications of artificial intelligence: the use of machine learning to extract useful information from multimodal data. It discusses the optimization methods that help minimize the error in developing patterns and classifications, which further helps improve prediction and decision-making. The book also presents formulations of real-world machine learning problems, and discusses AI solution methodologies as standalone or hybrid approaches. Lastly, it proposes novel metaheuristic methods to solve complex machine learning problems. Featuring valuable insights, the book helps readers explore new avenues leading toward multidisciplinary research discussions.

Linear Algebra and Optimization for Machine Learning

Charu C. Aggarwal
Linear Algebra and Optimization for Machine Learning Book PDF

Author : Charu C. Aggarwal
Publisher : Springer Nature
Page : 507 pages
File Size : 40,9 Mb
Release : 2020-05-13
Category : Computers
ISBN : 9783030403447

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Linear Algebra and Optimization for Machine Learning by Charu C. Aggarwal Pdf

This textbook introduces linear algebra and optimization in the context of machine learning. Examples and exercises are provided throughout the book. A solution manual for the exercises at the end of each chapter is available to teaching instructors. This textbook targets graduate level students and professors in computer science, mathematics and data science. Advanced undergraduate students can also use this textbook. The chapters for this textbook are organized as follows: 1. Linear algebra and its applications: The chapters focus on the basics of linear algebra together with their common applications to singular value decomposition, matrix factorization, similarity matrices (kernel methods), and graph analysis. Numerous machine learning applications have been used as examples, such as spectral clustering, kernel-based classification, and outlier detection. The tight integration of linear algebra methods with examples from machine learning differentiates this book from generic volumes on linear algebra. The focus is clearly on the most relevant aspects of linear algebra for machine learning and to teach readers how to apply these concepts. 2. Optimization and its applications: Much of machine learning is posed as an optimization problem in which we try to maximize the accuracy of regression and classification models. The “parent problem” of optimization-centric machine learning is least-squares regression. Interestingly, this problem arises in both linear algebra and optimization, and is one of the key connecting problems of the two fields. Least-squares regression is also the starting point for support vector machines, logistic regression, and recommender systems. Furthermore, the methods for dimensionality reduction and matrix factorization also require the development of optimization methods. A general view of optimization in computational graphs is discussed together with its applications to back propagation in neural networks. A frequent challenge faced by beginners in machine learning is the extensive background required in linear algebra and optimization. One problem is that the existing linear algebra and optimization courses are not specific to machine learning; therefore, one would typically have to complete more course material than is necessary to pick up machine learning. Furthermore, certain types of ideas and tricks from optimization and linear algebra recur more frequently in machine learning than other application-centric settings. Therefore, there is significant value in developing a view of linear algebra and optimization that is better suited to the specific perspective of machine learning.

Stochastic Optimization for Large-scale Machine Learning

Vinod Kumar Chauhan
Stochastic Optimization for Large scale Machine Learning Book PDF

Author : Vinod Kumar Chauhan
Publisher : CRC Press
Page : 189 pages
File Size : 50,7 Mb
Release : 2021-11-18
Category : Computers
ISBN : 9781000505610

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Stochastic Optimization for Large-scale Machine Learning by Vinod Kumar Chauhan Pdf

Advancements in the technology and availability of data sources have led to the `Big Data' era. Working with large data offers the potential to uncover more fine-grained patterns and take timely and accurate decisions, but it also creates a lot of challenges such as slow training and scalability of machine learning models. One of the major challenges in machine learning is to develop efficient and scalable learning algorithms, i.e., optimization techniques to solve large scale learning problems. Stochastic Optimization for Large-scale Machine Learning identifies different areas of improvement and recent research directions to tackle the challenge. Developed optimisation techniques are also explored to improve machine learning algorithms based on data access and on first and second order optimisation methods. Key Features: Bridges machine learning and Optimisation. Bridges theory and practice in machine learning. Identifies key research areas and recent research directions to solve large-scale machine learning problems. Develops optimisation techniques to improve machine learning algorithms for big data problems. The book will be a valuable reference to practitioners and researchers as well as students in the field of machine learning.

Hyperparameter Optimization in Machine Learning

Tanay Agrawal
Hyperparameter Optimization in Machine Learning Book PDF

Author : Tanay Agrawal
Publisher : Apress
Page : 166 pages
File Size : 49,8 Mb
Release : 2020-11-29
Category : Computers
ISBN : 1484265785

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Hyperparameter Optimization in Machine Learning by Tanay Agrawal Pdf

Dive into hyperparameter tuning of machine learning models and focus on what hyperparameters are and how they work. This book discusses different techniques of hyperparameters tuning, from the basics to advanced methods. This is a step-by-step guide to hyperparameter optimization, starting with what hyperparameters are and how they affect different aspects of machine learning models. It then goes through some basic (brute force) algorithms of hyperparameter optimization. Further, the author addresses the problem of time and memory constraints, using distributed optimization methods. Next you’ll discuss Bayesian optimization for hyperparameter search, which learns from its previous history. The book discusses different frameworks, such as Hyperopt and Optuna, which implements sequential model-based global optimization (SMBO) algorithms. During these discussions, you’ll focus on different aspects such as creation of search spaces and distributed optimization of these libraries. Hyperparameter Optimization in Machine Learning creates an understanding of how these algorithms work and how you can use them in real-life data science problems. The final chapter summaries the role of hyperparameter optimization in automated machine learning and ends with a tutorial to create your own AutoML script. Hyperparameter optimization is tedious task, so sit back and let these algorithms do your work. What You Will Learn Discover how changes in hyperparameters affect the model’s performance. Apply different hyperparameter tuning algorithms to data science problems Work with Bayesian optimization methods to create efficient machine learning and deep learning models Distribute hyperparameter optimization using a cluster of machines Approach automated machine learning using hyperparameter optimization Who This Book Is For Professionals and students working with machine learning.

Handbook of Machine Learning for Computational Optimization

Vishal Jain,Sapna Juneja,Abhinav Juneja,Ramani Kannan
Handbook of Machine Learning for Computational Optimization Book PDF

Author : Vishal Jain,Sapna Juneja,Abhinav Juneja,Ramani Kannan
Publisher : CRC Press
Page : 295 pages
File Size : 49,9 Mb
Release : 2021-11-02
Category : Technology & Engineering
ISBN : 9781000455670

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Handbook of Machine Learning for Computational Optimization by Vishal Jain,Sapna Juneja,Abhinav Juneja,Ramani Kannan Pdf

Focuses on new machine learning developments that can lead to newly developed applications Uses a predictive and futuristic approach which makes Machine Learning a promising tool for business processes and sustainable solutions Promotes newer algorithms which are more efficient and reliable for a new dimension in discovering certain latent domains of applications Discusses the huge potential in making better use of machines in order to ensure optimal prediction, execution, and decision-making Offers many real-time case studies

Machine Learning and Optimization Models for Optimization in Cloud

Punit Gupta,Mayank Kumar Goyal,Sudeshna Chakraborty,Ahmed A Elngar
Machine Learning and Optimization Models for Optimization in Cloud Book PDF

Author : Punit Gupta,Mayank Kumar Goyal,Sudeshna Chakraborty,Ahmed A Elngar
Publisher : CRC Press
Page : 232 pages
File Size : 49,8 Mb
Release : 2022-02-17
Category : Computers
ISBN : 9781000542257

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Machine Learning and Optimization Models for Optimization in Cloud by Punit Gupta,Mayank Kumar Goyal,Sudeshna Chakraborty,Ahmed A Elngar Pdf

Machine Learning and Models for Optimization in Cloud’s main aim is to meet the user requirement with high quality of service, least time for computation and high reliability. With increase in services migrating over cloud providers, the load over the cloud increases resulting in fault and various security failure in the system results in decreasing reliability. To fulfill this requirement cloud system uses intelligent metaheuristic and prediction algorithm to provide resources to the user in an efficient manner to manage the performance of the system and plan for upcoming requests. Intelligent algorithm helps the system to predict and find a suitable resource for a cloud environment in real time with least computational complexity taking into mind the system performance in under loaded and over loaded condition. This book discusses the future improvements and possible intelligent optimization models using artificial intelligence, deep learning techniques and other hybrid models to improve the performance of cloud. Various methods to enhance the directivity of cloud services have been presented which would enable cloud to provide better services, performance and quality of service to user. It talks about the next generation intelligent optimization and fault model to improve security and reliability of cloud. Key Features · Comprehensive introduction to cloud architecture and its service models. · Vulnerability and issues in cloud SAAS, PAAS and IAAS · Fundamental issues related to optimizing the performance in Cloud Computing using meta-heuristic, AI and ML models · Detailed study of optimization techniques, and fault management techniques in multi layered cloud. · Methods to improve reliability and fault in cloud using nature inspired algorithms and artificial neural network. · Advanced study of algorithms using artificial intelligence for optimization in cloud · Method for power efficient virtual machine placement using neural network in cloud · Method for task scheduling using metaheuristic algorithms. · A study of machine learning and deep learning inspired resource allocation algorithm for cloud in fault aware environment. This book aims to create a research interest & motivation for graduates degree or post-graduates. It aims to present a study on optimization algorithms in cloud for researchers to provide them with a glimpse of future of cloud computing in the era of artificial intelligence.

Black Box Optimization, Machine Learning, and No-Free Lunch Theorems

Panos M. Pardalos,Varvara Rasskazova,Michael N. Vrahatis
Black Box Optimization Machine Learning and No Free Lunch Theorems Book PDF

Author : Panos M. Pardalos,Varvara Rasskazova,Michael N. Vrahatis
Publisher : Springer Nature
Page : 388 pages
File Size : 42,9 Mb
Release : 2021-05-27
Category : Mathematics
ISBN : 9783030665159

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Black Box Optimization, Machine Learning, and No-Free Lunch Theorems by Panos M. Pardalos,Varvara Rasskazova,Michael N. Vrahatis Pdf

This edited volume illustrates the connections between machine learning techniques, black box optimization, and no-free lunch theorems. Each of the thirteen contributions focuses on the commonality and interdisciplinary concepts as well as the fundamentals needed to fully comprehend the impact of individual applications and problems. Current theoretical, algorithmic, and practical methods used are provided to stimulate a new effort towards innovative and efficient solutions. The book is intended for beginners who wish to achieve a broad overview of optimization methods and also for more experienced researchers as well as researchers in mathematics, optimization, operations research, quantitative logistics, data analysis, and statistics, who will benefit from access to a quick reference to key topics and methods. The coverage ranges from mathematically rigorous methods to heuristic and evolutionary approaches in an attempt to equip the reader with different viewpoints of the same problem.

Optimization for Data Analysis

Stephen J. Wright,Benjamin Recht
Optimization for Data Analysis Book PDF

Author : Stephen J. Wright,Benjamin Recht
Publisher : Cambridge University Press
Page : 239 pages
File Size : 53,5 Mb
Release : 2022-04-21
Category : Computers
ISBN : 9781316518984

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Optimization for Data Analysis by Stephen J. Wright,Benjamin Recht Pdf

A concise text that presents and analyzes the fundamental techniques and methods in optimization that are useful in data science.

Algorithms for Optimization

Mykel J. Kochenderfer,Tim A. Wheeler
Algorithms for Optimization Book PDF

Author : Mykel J. Kochenderfer,Tim A. Wheeler
Publisher : MIT Press
Page : 521 pages
File Size : 44,8 Mb
Release : 2019-03-12
Category : Computers
ISBN : 9780262039420

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Algorithms for Optimization by Mykel J. Kochenderfer,Tim A. Wheeler Pdf

A comprehensive introduction to optimization with a focus on practical algorithms for the design of engineering systems. This book offers a comprehensive introduction to optimization with a focus on practical algorithms. The book approaches optimization from an engineering perspective, where the objective is to design a system that optimizes a set of metrics subject to constraints. Readers will learn about computational approaches for a range of challenges, including searching high-dimensional spaces, handling problems where there are multiple competing objectives, and accommodating uncertainty in the metrics. Figures, examples, and exercises convey the intuition behind the mathematical approaches. The text provides concrete implementations in the Julia programming language. Topics covered include derivatives and their generalization to multiple dimensions; local descent and first- and second-order methods that inform local descent; stochastic methods, which introduce randomness into the optimization process; linear constrained optimization, when both the objective function and the constraints are linear; surrogate models, probabilistic surrogate models, and using probabilistic surrogate models to guide optimization; optimization under uncertainty; uncertainty propagation; expression optimization; and multidisciplinary design optimization. Appendixes offer an introduction to the Julia language, test functions for evaluating algorithm performance, and mathematical concepts used in the derivation and analysis of the optimization methods discussed in the text. The book can be used by advanced undergraduates and graduate students in mathematics, statistics, computer science, any engineering field, (including electrical engineering and aerospace engineering), and operations research, and as a reference for professionals.

Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition

Serkan Kiranyaz,Turker Ince,Moncef Gabbouj
Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition Book PDF

Author : Serkan Kiranyaz,Turker Ince,Moncef Gabbouj
Publisher : Springer Science & Business Media
Page : 343 pages
File Size : 52,9 Mb
Release : 2013-07-16
Category : Computers
ISBN : 9783642378461

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Multidimensional Particle Swarm Optimization for Machine Learning and Pattern Recognition by Serkan Kiranyaz,Turker Ince,Moncef Gabbouj Pdf

For many engineering problems we require optimization processes with dynamic adaptation as we aim to establish the dimension of the search space where the optimum solution resides and develop robust techniques to avoid the local optima usually associated with multimodal problems. This book explores multidimensional particle swarm optimization, a technique developed by the authors that addresses these requirements in a well-defined algorithmic approach. After an introduction to the key optimization techniques, the authors introduce their unified framework and demonstrate its advantages in challenging application domains, focusing on the state of the art of multidimensional extensions such as global convergence in particle swarm optimization, dynamic data clustering, evolutionary neural networks, biomedical applications and personalized ECG classification, content-based image classification and retrieval, and evolutionary feature synthesis. The content is characterized by strong practical considerations, and the book is supported with fully documented source code for all applications presented, as well as many sample datasets. The book will be of benefit to researchers and practitioners working in the areas of machine intelligence, signal processing, pattern recognition, and data mining, or using principles from these areas in their application domains. It may also be used as a reference text for graduate courses on swarm optimization, data clustering and classification, content-based multimedia search, and biomedical signal processing applications.

Non-convex Optimization for Machine Learning

Prateek Jain,Purushottam Kar
Non convex Optimization for Machine Learning Book PDF

Author : Prateek Jain,Purushottam Kar
Publisher : Foundations and Trends in Machine Learning
Page : 218 pages
File Size : 53,7 Mb
Release : 2017-12-04
Category : Machine learning
ISBN : 1680833685

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Non-convex Optimization for Machine Learning by Prateek Jain,Purushottam Kar Pdf

Non-convex Optimization for Machine Learning takes an in-depth look at the basics of non-convex optimization with applications to machine learning. It introduces the rich literature in this area, as well as equips the reader with the tools and techniques needed to apply and analyze simple but powerful procedures for non-convex problems. Non-convex Optimization for Machine Learning is as self-contained as possible while not losing focus of the main topic of non-convex optimization techniques. The monograph initiates the discussion with entire chapters devoted to presenting a tutorial-like treatment of basic concepts in convex analysis and optimization, as well as their non-convex counterparts. The monograph concludes with a look at four interesting applications in the areas of machine learning and signal processing, and exploring how the non-convex optimization techniques introduced earlier can be used to solve these problems. The monograph also contains, for each of the topics discussed, exercises and figures designed to engage the reader, as well as extensive bibliographic notes pointing towards classical works and recent advances. Non-convex Optimization for Machine Learning can be used for a semester-length course on the basics of non-convex optimization with applications to machine learning. On the other hand, it is also possible to cherry pick individual portions, such the chapter on sparse recovery, or the EM algorithm, for inclusion in a broader course. Several courses such as those in machine learning, optimization, and signal processing may benefit from the inclusion of such topics.