What is optimization?

Optimization is the act of selecting the best possible option to solve a mathematical problem when choosing from a set of variables. The concept of optimization has existed in mathematics for centuries, but in more recent times, scientists have discovered that other scientific disciplines have common elements, so the idea of optimization has carried over into other areas of study from engineering to economics to physics to biology. Optimization seeks to discover the maximum or minimum of a function to best solve a problem. It involves variables, constraints, and the objective function, or the goal that drives the solution to the problem. For example, in physics, an optimization problem might seek to discover the minimum amount of energy needed to achieve a certain objective. The advent of sophisticated computers has allowed mathematicians to achieve optimization more accurately across a wide range of functions and problems.

Why learn optimization?

Optimization helps data scientists and computer systems process data more effectively, estimate how many resources it will take to solve a problem, and search for the best solution based on the data available. These features make optimization a key tool that can help those who collect and analyze data. The methods of optimization work hand in hand with statistics and linear algebra to help you understand and process large amounts of data. Solving complex problems that rely on data becomes much easier when you apply the methods and formulas of optimization to them, which makes it essential for machine learning. In fact, today's computer science relies heavily on the relationship between machine learning and optimization. The principles of optimization apply in a different way in business, because nearly every business decision centers on maximizing or minimizing various factors and outcomes.

How can online courses on Coursera help me learn optimization?

When you study optimization with online courses on Coursera, you can gain a broad base of knowledge as well as applications that allow you to put what you learn into practice. Beginning courses include those in which you learn the basics of optimization, whereas more advanced courses go deeper into topics like stochastic processes, machine learning through specific applications, or how to estimate financial models.

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159 resultados para "optimization"

University of California, Davis
Search Engine Optimization (SEO)
Habilidades que obtendrás: Algorithms, Application Development, Back-End Web Development, Communication, Data Management, Data Model, Data Structures, Digital Marketing, Entrepreneurship, Forecasting, Influencing, Market Research, Marketing, Mathematical Optimization, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Project Management, Research and Design, Search Engine Optimization, Social Media, Software Architecture, Software Engineering, Software Testing, Strategy and Operations, Theoretical Computer Science, Web Development
4.6
(10.7k reseñas)
Beginner · Specialization
DeepLearning.AI
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization
Habilidades que obtendrás: Statistical Machine Learning, Artificial Neural Networks, Algorithms, Python Programming, Probability & Statistics, Applied Machine Learning, Mathematical Optimization, Hyperparameter, Mathematical Theory & Analysis, Machine Learning, Computer Programming, General Statistics, Big Data, Deep Learning, Tensorflow, Theoretical Computer Science, Mathematics, Statistical Programming, Data Management
4.9
(60.8k reseñas)
Intermediate · Course
University of Virginia
Pricing Strategy Optimization
Habilidades que obtendrás: Accounting, Business Analysis, Business Psychology, Customer Analysis, Design and Product, Entrepreneurship, Finance, General Statistics, Human Computer Interaction, Human Resources, Investment Management, Leadership Development, Leadership and Management, Management Accounting, Market Research, Marketing, Marketing Psychology, Mathematics, Pricing, Probability & Statistics, Product Management, Product Marketing, Research and Design, Sales, Strategy, Strategy and Operations, User Research
4.8
(789 reseñas)
Beginner · Specialization
University of California, Irvine
Mobile Marketing, Optimization Tactics, and Analytics
Habilidades que obtendrás: Research and Design, Supply Chain and Logistics, Market Analysis, Strategy and Operations, Data Analysis, Marketing Strategy, Leadership and Management, Programming Principles, Back-End Web Development, Planning, Market Research, Strategy, Sales, Communication, Computer Programming, Entrepreneurship, Web Development, Marketing, Web, Digital Marketing
4.8
(24 reseñas)
Beginner · Course
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En síntesis, estos son los 10 cursos más populares optimization

Search Engine Optimization (SEO): University of California, Davis
Improving Deep Neural Networks: Hyperparameter Tuning, Regularization and Optimization: DeepLearning.AI
Advanced Search Engine Optimization Strategies: University of California, Davis
Optimization Methods in Asset Management: Columbia University
Optimization for Decision Making: University of Minnesota
Search Engine Optimization (SEO) with Squarespace: Coursera Project Network
Pricing Strategy Optimization: University of Virginia
Cloud Cost Management: Optimization Strategies: Amazon Web Services
Mobile Marketing, Optimization Tactics, and Analytics: University of California, Irvine
Portfolio Optimization using Markowitz Model: Coursera Project Network

Habilidades que puedes aprender en Algorithms

Gráficos (22)

Optimización Matemática (21)

Programa De Computadora (20)

Estructura De Datos (19)

Resolución De Problemas (19)

Álgebra (12)

Visión Artificial (10)

Matemáticas Discretas (10)

Teoría De Grafos (10)

Procesamiento De Imágenes (10)

Álgebra Lineal (10)

Aprendizaje por refuerzo (10)

Preguntas frecuentes sobre Optimización

Optimization is the act of selecting the best possible option to solve a mathematical problem when choosing from a set of variables. The concept of optimization has existed in mathematics for centuries, but in more recent times, scientists have discovered that other scientific disciplines have common elements, so the idea of optimization has carried over into other areas of study from engineering to economics to physics to biology. Optimization seeks to discover the maximum or minimum of a function to best solve a problem. It involves variables, constraints, and the objective function, or the goal that drives the solution to the problem. For example, in physics, an optimization problem might seek to discover the minimum amount of energy needed to achieve a certain objective. The advent of sophisticated computers has allowed mathematicians to achieve optimization more accurately across a wide range of functions and problems.‎
Optimization helps data scientists and computer systems process data more effectively, estimate how many resources it will take to solve a problem, and search for the best solution based on the data available. These features make optimization a key tool that can help those who collect and analyze data. The methods of optimization work hand in hand with statistics and linear algebra to help you understand and process large amounts of data. Solving complex problems that rely on data becomes much easier when you apply the methods and formulas of optimization to them, which makes it essential for machine learning. In fact, today's computer science relies heavily on the relationship between machine learning and optimization. The principles of optimization apply in a different way in business, because nearly every business decision centers on maximizing or minimizing various factors and outcomes.‎
When you study optimization with online courses on Coursera, you can gain a broad base of knowledge as well as applications that allow you to put what you learn into practice. Beginning courses include those in which you learn the basics of optimization, whereas more advanced courses go deeper into topics like stochastic processes, machine learning through specific applications, or how to estimate financial models.‎

Se puso a disposición este contenido de preguntas frecuentes solo con fines informativos. Se les pide a los estudiantes que realicen investigaciones adicionales para asegurarse de que los cursos y otras credenciales cumplan con sus objetivos personales, profesionales y financieros.