Name: Optimization Fundamentals
Availability: InStock

Course Overview

Optimization drives solutions across virtually every area of data science, machine learning, and predictive modeling. Whether powering recommendation systems, solving large-scale data matching, or refining algorithms, optimization provides the tools to make systems efficient and scalable.

In this course, you will explore key optimization techniques, including gradient descent algorithms, constrained and unconstrained methods, and stochastic approaches like stochastic gradient descent (SGD). You'll adapt these tools to address high-impact computational challenges with precision and confidence. By the end of the course, you'll understand optimization strategies that are essential for tackling real-world problems effectively.

Key Course Takeaways

Recognize and classify optimization problems as constrained or unconstrained and formulate solutions using mathematical structures
Implement gradient descent to optimize functions and analyze algorithmic convergence properties
Execute stochastic gradient descent algorithms for machine learning applications and compare SGD techniques to traditional gradient methods

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How It Works

Course Length

2 weeks

Effort

8 to 10 hours of study per week

Format

100% online, instructor-led

Course Authors

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David Bindel

Professor

Cornell Bowers Computing and Information Science

Professor, Cornell Bowers Computing and Information Science

David S. Bindel is a Professor of Computer Science and Director of the Center for Applied Math. Professor Bindel works at the interface of computational science and engineering, and his research mixes mathematical analysis, application modeling, and software design. His active research areas include optimizing stellarators, verified numerics, kernel methods, parallel surrogate optimization, spectral network analysis, nonlinear eigenvalue bounds, and nonlinear waves in resonant MEMS.

Professor Bindel received his Ph.D. in Computer Science from the University of California, Berkeley, and his B.S. in Math and Computer Science from the University of Maryland, College Park. He is a SIAM Fellow and Sloan Fellow.

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Anil Damle

Associate Professor

Cornell Bowers Computing and Information Science

Associate Professor, Cornell Bowers Computing and Information Science

Anil Damle is an Associate Professor of Computer Science. Professor Damle’s research focuses on the development and analysis of robust and efficient algorithms in applied and computational mathematics that exploit structure coming from underlying physical or statistical models. He interfaces with a broad range of application areas and his work is inherently interdisciplinary, with the ultimate goal of developing algorithms that are usable for practitioners.

Professor Damle received his Ph.D. from Stanford University in Computational and Mathematical Engineering and his M.A. in Applied Mathematics and B.S. in applied mathematics and computer engineering from the University of Colorado, Boulder.

Who Should Enroll

Software engineers building AI-powered applications
Data analysts and scientists working with large-scale datasets
Engineers applying computational methods to complex systems
Web and frontend developers integrating machine learning features
Computational biologists and scientific researchers modeling real-world phenomena Investment managers leveraging quantitative analysis
Game developers optimizing physics engines and AI behaviors
Anyone in a technical role seeking to strengthen their mathematical foundation for AI and machine learning

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Applied Mathematics for AI and Data Science

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