Machine Learning Under A Modern Optimization Lens PDF Download
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Author | : Dimitris Bertsimas |
Publisher | : |
Total Pages | : 589 |
Release | : 2019 |
Genre | : Machine learning |
ISBN | : 9781733788502 |
Download Machine Learning Under a Modern Optimization Lens Book in PDF, ePub and Kindle
Author | : Dimitris Bertsimas |
Publisher | : |
Total Pages | : 602 |
Release | : 2005 |
Genre | : Algorithms |
ISBN | : 9780975914625 |
Download Optimization Over Integers Book in PDF, ePub and Kindle
Author | : Jeremy Watt |
Publisher | : Cambridge University Press |
Total Pages | : 597 |
Release | : 2020-01-09 |
Genre | : Computers |
ISBN | : 1108480721 |
Download Machine Learning Refined Book in PDF, ePub and Kindle
An intuitive approach to machine learning covering key concepts, real-world applications, and practical Python coding exercises.
Author | : Jianzhong Wang |
Publisher | : Springer Science & Business Media |
Total Pages | : 363 |
Release | : 2012-04-28 |
Genre | : Computers |
ISBN | : 3642274978 |
Download Geometric Structure of High-Dimensional Data and Dimensionality Reduction Book in PDF, ePub and Kindle
"Geometric Structure of High-Dimensional Data and Dimensionality Reduction" adopts data geometry as a framework to address various methods of dimensionality reduction. In addition to the introduction to well-known linear methods, the book moreover stresses the recently developed nonlinear methods and introduces the applications of dimensionality reduction in many areas, such as face recognition, image segmentation, data classification, data visualization, and hyperspectral imagery data analysis. Numerous tables and graphs are included to illustrate the ideas, effects, and shortcomings of the methods. MATLAB code of all dimensionality reduction algorithms is provided to aid the readers with the implementations on computers. The book will be useful for mathematicians, statisticians, computer scientists, and data analysts. It is also a valuable handbook for other practitioners who have a basic background in mathematics, statistics and/or computer algorithms, like internet search engine designers, physicists, geologists, electronic engineers, and economists. Jianzhong Wang is a Professor of Mathematics at Sam Houston State University, U.S.A.
Author | : Suvrit Sra |
Publisher | : MIT Press |
Total Pages | : 509 |
Release | : 2012 |
Genre | : Computers |
ISBN | : 026201646X |
Download Optimization for Machine Learning Book in PDF, ePub and Kindle
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.
Author | : Frank Hutter |
Publisher | : Springer |
Total Pages | : 223 |
Release | : 2019-05-17 |
Genre | : Computers |
ISBN | : 3030053180 |
Download Automated Machine Learning Book in PDF, ePub and Kindle
This open access book presents the first comprehensive overview of general methods in Automated Machine Learning (AutoML), collects descriptions of existing systems based on these methods, and discusses the first series of international challenges of AutoML systems. The recent success of commercial ML applications and the rapid growth of the field has created a high demand for off-the-shelf ML methods that can be used easily and without expert knowledge. However, many of the recent machine learning successes crucially rely on human experts, who manually select appropriate ML architectures (deep learning architectures or more traditional ML workflows) and their hyperparameters. To overcome this problem, the field of AutoML targets a progressive automation of machine learning, based on principles from optimization and machine learning itself. This book serves as a point of entry into this quickly-developing field for researchers and advanced students alike, as well as providing a reference for practitioners aiming to use AutoML in their work.
Author | : Jason Brownlee |
Publisher | : Machine Learning Mastery |
Total Pages | : 412 |
Release | : 2021-09-22 |
Genre | : Computers |
ISBN | : |
Download Optimization for Machine Learning Book in PDF, ePub and Kindle
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.
Author | : Felipe Leno da Silva |
Publisher | : Springer |
Total Pages | : 111 |
Release | : 2021-05-27 |
Genre | : Computers |
ISBN | : 9783031004636 |
Download Transfer Learning for Multiagent Reinforcement Learning Systems Book in PDF, ePub and Kindle
Learning to solve sequential decision-making tasks is difficult. Humans take years exploring the environment essentially in a random way until they are able to reason, solve difficult tasks, and collaborate with other humans towards a common goal. Artificial Intelligent agents are like humans in this aspect. Reinforcement Learning (RL) is a well-known technique to train autonomous agents through interactions with the environment. Unfortunately, the learning process has a high sample complexity to infer an effective actuation policy, especially when multiple agents are simultaneously actuating in the environment. However, previous knowledge can be leveraged to accelerate learning and enable solving harder tasks. In the same way humans build skills and reuse them by relating different tasks, RL agents might reuse knowledge from previously solved tasks and from the exchange of knowledge with other agents in the environment. In fact, virtually all of the most challenging tasks currently solved by RL rely on embedded knowledge reuse techniques, such as Imitation Learning, Learning from Demonstration, and Curriculum Learning. This book surveys the literature on knowledge reuse in multiagent RL. The authors define a unifying taxonomy of state-of-the-art solutions for reusing knowledge, providing a comprehensive discussion of recent progress in the area. In this book, readers will find a comprehensive discussion of the many ways in which knowledge can be reused in multiagent sequential decision-making tasks, as well as in which scenarios each of the approaches is more efficient. The authors also provide their view of the current low-hanging fruit developments of the area, as well as the still-open big questions that could result in breakthrough developments. Finally, the book provides resources to researchers who intend to join this area or leverage those techniques, including a list of conferences, journals, and implementation tools. This book will be useful for a wide audience; and will hopefully promote new dialogues across communities and novel developments in the area.
Author | : Dimitris Bertsimas |
Publisher | : |
Total Pages | : 462 |
Release | : 2016 |
Genre | : Computer simulation |
ISBN | : 9780989910897 |
Download The Analytics Edge Book in PDF, ePub and Kindle
"Provides a unified, insightful, modern, and entertaining treatment of analytics. The book covers the science of using data to build models, improve decisions, and ultimately add value to institutions and individuals"--Back cover.
Author | : Prateek Jain |
Publisher | : Foundations and Trends in Machine Learning |
Total Pages | : 218 |
Release | : 2017-12-04 |
Genre | : Machine learning |
ISBN | : 9781680833683 |
Download Non-convex Optimization for Machine Learning Book in PDF, ePub and Kindle
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.