Optimization Techniques 2024
GEORGIOS ZOIS
The course provides an overview of optimization tools and techniques, motivated by large-scale problems that arise in Data Science. Optimization is at the heart of various critical tasks related to handling big data (e.g. classification problems, machine learning, discrete optimization, etc), and a great number of methodologies have been developed over the years. The course aims at first to illustrate how we can model problems as optimization questions. We will then examine a variety of techniques that are currently used for solving such problems in practice (including among others, linear programming and convex programming techniques, combinatorial algorithms, local search methods and genetic algorithms).
ΛιγότεραThe course provides an overview of optimization tools and techniques, motivated by large-scale problems that arise in Data Science. Optimization is at the heart of various critical tasks related to handling big data (e.g. classification problems, machine learning, discrete optimization, etc), and a great number of methodologies have been developed over the years. The course aims at first to illustrate how we can model problems as optimization questions. We will then examine a variety of techniques that are currently used for solving such problems in practice (including among others, linear programming and convex programming techniques, combinatorial algorithms, local search methods and genetic algorithms).
The course provides an overview of optimization tools and techniques, motivated by large-scale problems that arise in Data Science. Optimization is at the heart of various critical tasks related to handling big data (e.g. classification problems, machine learning, discrete optimization, etc), and a great number of methodologies have been developed over the years. The course aims at first to illustrate how we can model problems as optimization questions. We will then examine a variety of techniques that are currently used for solving such problems in practice (including among others, linear programming and convex programming techniques, combinatorial algorithms, local search methods and genetic algorithms).
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