An Introduction to Optimization with Applications in Machine Learning and Data Analytics

Bibliographic Details
Title: An Introduction to Optimization with Applications in Machine Learning and Data Analytics
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.
Authors: Jeffrey Paul Wheeler
Resource Type: eBook.
Subjects: Machine learning--Mathematics--Textbooks, Mathematical optimization--Data processing--Textbooks, Mathematical optimization--Textbooks
Categories: MATHEMATICS / Optimization, MATHEMATICS / General
Database: eBook Index
FullText Text:
  Availability: 0
Header DbId: edsebk
DbLabel: eBook Index
An: 3712157
RelevancyScore: 969
AccessLevel: 6
PubType: eBook
PubTypeId: ebook
PreciseRelevancyScore: 968.509704589844
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  Data: An Introduction to Optimization with Applications in Machine Learning and Data Analytics
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  Data: 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.
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RecordInfo BibRecord:
  BibEntity:
    Classifications:
      – Code: 519.60285631
        Scheme: ddc
        Type: prePub
    Languages:
      – Code: eng
        Text: English
    Subjects:
      – SubjectFull: Machine learning--Mathematics--Textbooks
        Type: general
      – SubjectFull: Mathematical optimization--Data processing--Textbooks
        Type: general
      – SubjectFull: Mathematical optimization--Textbooks
        Type: general
    Titles:
      – TitleFull: An Introduction to Optimization with Applications in Machine Learning and Data Analytics
        Type: main
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          Name:
            NameFull: Jeffrey Paul Wheeler
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            NameFull: Jeffrey Paul Wheeler
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          Dates:
            – D: 01
              M: 01
              Type: published
              Y: 2023
            – D: 17
              M: 02
              Type: profile
              Y: 2024
          Identifiers:
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              Value: 9780367425500
            – Type: isbn-print
              Value: 9781032615905
            – Type: isbn-electronic
              Value: 9780367425517
            – Type: isbn-electronic
              Value: 9781003803591
            – Type: isbn-electronic
              Value: 9781003803676
          Titles:
            – TitleFull: An Introduction to Optimization with Applications in Machine Learning and Data Analytics
              Type: main
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