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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325 résultats pour ''optimization'

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The University of Melbourne
Discrete Optimization
Compétences que vous acquerrez : Computer Science, Mathematics, Applied Mathematics, Computer Programming, Geometry, Linear Algebra, Python Programming, Algorithms, Entrepreneurship, Graph Theory, Market Research, Operations Research, Programming Principles, Research and Design, Software Engineering, Software Testing, Statistical Programming, Strategy and Operations, Theoretical Computer Science
4.8
(702 avis)
Intermediate · Course · 1-3 Months
Imperial College London
Mathematics for Machine Learning
Compétences que vous acquerrez : Mathematics, Algebra, Linear Algebra, Machine Learning, Python Programming, Probability & Statistics, General Statistics, Calculus, Computer Programming, Applied Mathematics, Mathematical Theory & Analysis, Statistical Programming, Algorithms, Dimensionality Reduction, Regression, Theoretical Computer Science, Basic Descriptive Statistics, Data Analysis, Probability Distribution, Artificial Neural Networks, Computer Graphic Techniques, Computer Graphics, Computer Networking, Deep Learning, Differential Equations, Experiment, Machine Learning Algorithms, Network Model
4.6
(13.4k avis)
Beginner · Specialization · 3-6 Months
Gratuit
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Gratuit
Stanford University
Game Theory II: Advanced Applications
Compétences que vous acquerrez : Game Theory, Mathematics, Algebra, Algorithms, Amazon Web Services, Applied Machine Learning, Applied Mathematics, ArcGIS, Artificial Neural Networks, Cloud Computing, Design and Product, Entrepreneurship, Leadership and Management, Machine Learning, Market Research, Marketing, Product Management, Product Marketing, Research and Design, Sales, Statistical Programming, Strategy, Strategy and Operations, Theoretical Computer Science
4.7
(592 avis)
Advanced · Course · 1-3 Months
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En résumé, voici 10 de nos cours optimization les plus populaires

Operations Research (1): Models and Applications: National Taiwan University
Discrete Optimization: The University of Melbourne
Mathematics for Machine Learning: Imperial College London
Operations Research (2): Optimization Algorithms: National Taiwan University
Optimization Methods in Asset Management: Columbia University
Supply Chain Optimization: University of California, Irvine
Optimization for Decision Making: University of Minnesota
Operations Research (3): Theory: National Taiwan University
Game Theory II: Advanced Applications: Stanford University
离散优化建模基础篇 Basic Modeling for Discrete Optimization: The Chinese University of Hong Kong

Compétences que vous avez acquises en Algorithms

Graphes (22)

Optimisation Mathématique (21)

Programme Informatique (20)

Structure Des Données (19)

Résolution De Problèmes (19)

Algèbre (12)

Vision Par Ordinateur (10)

Mathématiques Discrètes (10)

Théorie Des Graphes (10)

Traitement De L'image (10)

Algèbre Linéaire (10)

Apprentissage Par Renforcement (10)

Questions fréquentes sur Optimisation

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.‎

Le contenu de cette FAQ a été mis à disposition à des fins d'information uniquement. Il est conseillé aux étudiants d'effectuer des recherches supplémentaires afin de s'assurer que les cours et autres qualifications suivis correspondent à leurs objectifs personnels, professionnels et financiers.