Applied Optimization: Linear, Nonlinear, & ML Focus

Model, solve, and code real-world optimization problems and explore their role in machine learning

Description

Unlock the power of optimization with this practical, hands-on course designed for engineers, students, researchers, and anyone eager to solve real-world problems using mathematical optimization techniques.

This course begins with the fundamentals—what optimization is, why it’s important, and how to formulate real-world problems as mathematical models. You’ll explore different types of optimization problems, including linear, nonlinear, constrained, and unconstrained cases.

We guide you step by step through solving linear optimization problems using both Python (with SciPy) and MATLAB, providing clear explanations and code walkthroughs. You’ll then dive into nonlinear constrained optimization using the Lagrange multiplier method, followed by an in-depth look at gradient descent algorithms for single-variable and multivariable functions.

Throughout the course, you’ll learn how to implement these techniques from scratch and using built-in functions, making it ideal for learners who want both conceptual clarity and practical coding skills.

The final lecture explores how optimization plays a central role in machine learning, especially in training models and minimizing cost functions.

Whether you’re an engineering student, data science enthusiast, or academic researcher, this course equips you with the tools and confidence to solve optimization problems in MATLAB and Python.

Start learning today and build a strong foundation in applied optimization!

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