Introduction to Mathematical Optimization

Stanford Continuing Studies

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Stanford Continuing Studies

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About the course

Mathematical optimization underpins many applications in science and engineering, as it provides a set of formal tools to compute the ‘best’ action, design, control, or model from a set of possibilities. In data science and machine learning, mathematical optimization is the engine of model fitting. This workshop will provide an overview of the key elements of this topic (unconstrained, constrained, convex optimization, optimization for model fitting), and will have a practical focus, with participants formulating and solving optimization problems early and often using standard modeling languages and solvers. By introducing common models from machine learning and other fields, this workshop aims to make participants comfortable with optimization modeling so that they may use it for rapid prototyping and experimentation in their own work.

Topics to be discussed in this workshop include: formulating optimization problems; fundamentals of constrained and unconstrained optimization; convex optimization; optimization methods for model fitting in machine learning optimization in Python using SciPy and CVXPY; and in-depth Jupyter Notebook examples from machine learning, statistics, and other fields.

Prerequisite: Students should be comfortable with linear algebra, differential multivariable calculus, and basic probability and statistics. Experience with Python will be helpful, but not required.

Experts

Kevin Carlberg

Kevin Carlberg is an AI Research Science Manager Facebook Reality Labs and an Affiliate Associate Professor of Applied Mathematics and Mechanical Engineering at the University of Washington . He leads a research team focused on enabling the future of augmented and virtual reality through AI-drive...

Introduction to Mathematical Optimization at Stanford Continuing Studies

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