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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178 résultats pour ''optimization'

Gratuit
Gratuit
Gratuit
Imperial College London
Mathematics for Machine Learning
Compétences que vous acquerrez : Algebra, Algorithms, Artificial Neural Networks, Basic Descriptive Statistics, Calculus, Computer Graphic Techniques, Computer Graphics, Computer Programming, Data Analysis, Deep Learning, Differential Equations, General Statistics, Lambda Calculus, Linear Algebra, Machine Learning, Machine Learning Algorithms, Mathematical Theory & Analysis, Mathematics, Matrices, Probability & Statistics, Probability Distribution, Python Programming, Regression, Statistical Programming, Theoretical Computer Science
4.6
(12.8k avis)
Beginner · Specialization · 3-6 Months
Gratuit
University of Pennsylvania
Fintech: Foundations & Applications of Financial Technology
Compétences que vous acquerrez : Algorithms, BlockChain, Business Process Management, Cryptocurrency, Entrepreneurial Finance, Entrepreneurship, FinTech, Finance, Investment Management, Machine Learning, Machine Learning Algorithms, Payments, Regulations and Compliance, Risk Management, Strategy and Operations, Theoretical Computer Science
4.6
(3k avis)
Beginner · Specialization · 3-6 Months
Gratuit
Gratuit
University of Pennsylvania
Modeling Risk and Realities
Compétences que vous acquerrez : Data Model, Data Management, Leadership and Management, Mathematical Optimization, Data Analysis, Probability, Statistical Tests, Probability & Statistics, Research and Design, Risk Management, Basic Descriptive Statistics, Risk, Correlation And Dependence, Probability Distribution, General Statistics, Simulation, Finance, Problem Solving, Statistical Analysis, Spreadsheet Software, Microsoft Excel, Entrepreneurship, Modeling, Business Analysis, Analysis
4.6
(2.1k avis)
Mixed · Course · 1-4 Weeks

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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
Operations Research (2): Optimization Algorithms: National Taiwan University
Mathematics for Machine Learning: Imperial College London
Operations Research (3): Theory: National Taiwan University
Fintech: Foundations & Applications of Financial Technology: University of Pennsylvania
Differential Calculus through Data and Modeling: Johns Hopkins University
Basic Modeling for Discrete Optimization: The University of Melbourne
Solving Algorithms for Discrete Optimization: The University of Melbourne
Modeling Risk and Realities: University of Pennsylvania

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.