Author: Jeffrey Paul Wheeler Publisher: CRC Press ISBN: 1003803598 Category : Mathematics Languages : en Pages : 475
Book Description
The primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields. This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course. The book contains many practical applications but also explains the mathematics behind the techniques, including stating definitions and proving theorems. Optimization techniques are at the heart of the first spam filters, are used in self-driving cars, play a great role in machine learning, and can be used in such places as determining a batting order in a Major League Baseball game. Additionally, optimization has seemingly limitless other applications in business and industry. In short, knowledge of this subject offers an individual both a very marketable skill set for a wealth of jobs as well as useful tools for research in many academic disciplines. Many of the problems rely on using a computer. Microsoft’s Excel is most often used, as this is common in business, but Python and other languages are considered. The consideration of other programming languages permits experienced mathematics and engineering students to use MATLAB® or Mathematica, and the computer science students to write their own programs in Java or Python.
Author: Giuseppe C. Calafiore Publisher: Cambridge University Press ISBN: 1107050871 Category : Business & Economics Languages : en Pages : 651
Book Description
This accessible textbook demonstrates how to recognize, simplify, model and solve optimization problems - and apply these principles to new projects.
Author: Jeffrey Paul Wheeler Publisher: CRC Press ISBN: 1003803679 Category : Mathematics Languages : en Pages : 891
Book Description
The primary goal of this text is a practical one. Equipping students with enough knowledge and creating an independent research platform, the author strives to prepare students for professional careers. Providing students with a marketable skill set requires topics from many areas of optimization. The initial goal of this text is to develop a marketable skill set for mathematics majors as well as for students of engineering, computer science, economics, statistics, and business. Optimization reaches into many different fields. This text provides a balance where one is needed. Mathematics optimization books are often too heavy on theory without enough applications; texts aimed at business students are often strong on applications, but weak on math. The book represents an attempt at overcoming this imbalance for all students taking such a course. The book contains many practical applications but also explains the mathematics behind the techniques, including stating definitions and proving theorems. Optimization techniques are at the heart of the first spam filters, are used in self-driving cars, play a great role in machine learning, and can be used in such places as determining a batting order in a Major League Baseball game. Additionally, optimization has seemingly limitless other applications in business and industry. In short, knowledge of this subject offers an individual both a very marketable skill set for a wealth of jobs as well as useful tools for research in many academic disciplines. Many of the problems rely on using a computer. Microsoft’s Excel is most often used, as this is common in business, but Python and other languages are considered. The consideration of other programming languages permits experienced mathematics and engineering students to use MATLAB® or Mathematica, and the computer science students to write their own programs in Java or Python.
Author: Dirk P. Kroese Publisher: CRC Press ISBN: 1000730778 Category : Business & Economics Languages : en Pages : 538
Book Description
Focuses on mathematical understanding Presentation is self-contained, accessible, and comprehensive Full color throughout Extensive list of exercises and worked-out examples Many concrete algorithms with actual code
Author: Andrew Kelleher Publisher: Addison-Wesley Professional ISBN: 0134116569 Category : Computers Languages : en Pages : 465
Book Description
Foundational Hands-On Skills for Succeeding with Real Data Science Projects This pragmatic book introduces both machine learning and data science, bridging gaps between data scientist and engineer, and helping you bring these techniques into production. It helps ensure that your efforts actually solve your problem, and offers unique coverage of real-world optimization in production settings. –From the Foreword by Paul Dix, series editor Machine Learning in Production is a crash course in data science and machine learning for people who need to solve real-world problems in production environments. Written for technically competent “accidental data scientists” with more curiosity and ambition than formal training, this complete and rigorous introduction stresses practice, not theory. Building on agile principles, Andrew and Adam Kelleher show how to quickly deliver significant value in production, resisting overhyped tools and unnecessary complexity. Drawing on their extensive experience, they help you ask useful questions and then execute production projects from start to finish. The authors show just how much information you can glean with straightforward queries, aggregations, and visualizations, and they teach indispensable error analysis methods to avoid costly mistakes. They turn to workhorse machine learning techniques such as linear regression, classification, clustering, and Bayesian inference, helping you choose the right algorithm for each production problem. Their concluding section on hardware, infrastructure, and distributed systems offers unique and invaluable guidance on optimization in production environments. Andrew and Adam always focus on what matters in production: solving the problems that offer the highest return on investment, using the simplest, lowest-risk approaches that work. Leverage agile principles to maximize development efficiency in production projects Learn from practical Python code examples and visualizations that bring essential algorithmic concepts to life Start with simple heuristics and improve them as your data pipeline matures Avoid bad conclusions by implementing foundational error analysis techniques Communicate your results with basic data visualization techniques Master basic machine learning techniques, starting with linear regression and random forests Perform classification and clustering on both vector and graph data Learn the basics of graphical models and Bayesian inference Understand correlation and causation in machine learning models Explore overfitting, model capacity, and other advanced machine learning techniques Make informed architectural decisions about storage, data transfer, computation, and communication Register your book for convenient access to downloads, updates, and/or corrections as they become available. See inside book for details.
Author: Steven L. Brunton Publisher: Cambridge University Press ISBN: 1009098489 Category : Computers Languages : en Pages : 615
Book Description
A textbook covering data-science and machine learning methods for modelling and control in engineering and science, with Python and MATLAB®.
Author: Carlos Andre Reis Pinheiro Publisher: SAS Institute ISBN: 1953329624 Category : Computers Languages : en Pages : 169
Book Description
Boost your understanding of data science techniques to solve real-world problems Data science is an exciting, interdisciplinary field that extracts insights from data to solve business problems. This book introduces common data science techniques and methods and shows you how to apply them in real-world case studies. From data preparation and exploration to model assessment and deployment, this book describes every stage of the analytics life cycle, including a comprehensive overview of unsupervised and supervised machine learning techniques. The book guides you through the necessary steps to pick the best techniques and models and then implement those models to successfully address the original business need. No software is shown in the book, and mathematical details are kept to a minimum. This allows you to develop an understanding of the fundamentals of data science, no matter what background or experience level you have.
Author: John D. Kelleher Publisher: MIT Press ISBN: 0262361108 Category : Computers Languages : en Pages : 853
Book Description
The second edition of a comprehensive introduction to machine learning approaches used in predictive data analytics, covering both theory and practice. Machine learning is often used to build predictive models by extracting patterns from large datasets. These models are used in predictive data analytics applications including price prediction, risk assessment, predicting customer behavior, and document classification. This introductory textbook offers a detailed and focused treatment of the most important machine learning approaches used in predictive data analytics, covering both theoretical concepts and practical applications. Technical and mathematical material is augmented with explanatory worked examples, and case studies illustrate the application of these models in the broader business context. This second edition covers recent developments in machine learning, especially in a new chapter on deep learning, and two new chapters that go beyond predictive analytics to cover unsupervised learning and reinforcement learning.
Author: Roy, Manikant Publisher: IGI Global ISBN: 1799871908 Category : Computers Languages : en Pages : 241
Book Description
Data analytics is proving to be an ally for epidemiologists as they join forces with data scientists to address the scale of crises. Analytics examined from many sources can derive insights and be used to study and fight global outbreaks. Pandemic analytics is a modern way to combat a problem as old as humanity itself: the proliferation of disease. Machine Learning and Data Analytics for Predicting, Managing, and Monitoring Disease explores different types of data and discusses how to prepare data for analysis, perform simple statistical analyses, create meaningful data visualizations, predict future trends from data, and more by applying cutting edge technology such as machine learning and data analytics in the wake of the COVID-19 pandemic. Covering a range of topics such as mental health analytics during COVID-19, data analysis and machine learning using Python, and statistical model development and deployment, it is ideal for researchers, academicians, data scientists, technologists, data analysts, diagnosticians, healthcare professionals, computer scientists, and students.
Author: Jeffrey Paul Wheeler
Author: Stephen J. Wright
Author: Giuseppe C. Calafiore
Author: Jeffrey Paul Wheeler
Author: Dirk P. Kroese
Author: Andrew Kelleher
Author: Steven L. Brunton
Author: Carlos Andre Reis Pinheiro
Author: John D. Kelleher
Author: Roy, Manikant