Modeling and Optimization for Machine Learning

Monday, June 21, 2021 (8:00 AM - 5:00 PM) (EDT)


Master the data tools you need—from numerical linear algebra to convex programming—to make smarter decisions and drive enhanced results. With the guidance of top experts, you’ll gain a greater understanding of how to apply cutting-edge digital strategies to practical vision, learning, and graphics challenges. You’ll then apply your newfound skills by taking part in hands-on coding and mathematical exercises designed to strengthen your knowledge. Learn more:

Participant Takeaways 

Participants in the course will learn how to: 

- Recognize classes of optimization problems in machine learning and related disciplines. 

- Learn concepts that demystify the “why” and “how” of ubiquitous topics such as regression, deep learning, and large-scale optimization, with a focus on convex and non-convex models. 

- The Interface with software for computing optimal solutions to a given machine learning problem. 

- Understand the mathematical underpinnings of optimization methods via examples drawn from machine learning, computer vision, engineering, and data analysis. 

- Understand foundational optimization ideas including gradient descent, stochastic gradient methods, higher-order methods, and more advanced optimization algorithms. 

- Classify optimization problems by their tractability, difficulty, and compatibility with existing software. 

- Learn to cut through the hype to make more informed choices for their own applications. 

Did you know that as a Mass TLC member, you can receive a discount on MIT Professional Education - Short Programs and Digital Plus Programs? Use the MassTLC10 code at the time of registration to receive a 10% discount. 
Register and learn more:

Live Virtual Program
Event Contact
Daniel Parent
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Monday, June 21, 2021 (8:00 AM - 5:00 PM) (EDT)
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