Applied Optimization: Linear, Nonlinear, & Ml Focus

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

What you'll learn

Understand core optimization concepts and mathematical modeling techniques

Apply linear and nonlinear optimization techniques using MATLAB and Python

Implement gradient descent algorithms for single-variable and multi-variable problems

Recognize the role of optimization in machine learning

Requirements

It is better if have some knowledge on Python and MATLAB. If you dont have not an issue

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!

Overview

Section 1: Course Overview

Lecture 1 Course Overview

Section 2: Introduction

Lecture 2 Human Learning Vs. Machine Learning

Lecture 3 What is Optimization?

Lecture 4 Types of Optimization Problems

Section 3: Linear Optimization

Lecture 5 Mathematical modelling of linear optimization problem - Case Study 1

Lecture 6 Session 6 - Python Implementation - Linear Optimization - Case Study 1

Lecture 7 Session 7 -MATLAB Implementation - Linear Optimization - Case Study 1

Lecture 8 Mathematical modelling of linear optimization problem - Case Study 2

Lecture 9 Session 9 - Case Study 2 - Python Implementation

Lecture 10 Session 10 - Case Study 2- MATLAB Implementation

Section 4: Constrained Non-Linear Optimization

Lecture 11 Session-11 Constrained Non Linear Optimization - Lagrange multiplier method

Lecture 12 Case Study - 1 : Economic Load Dispatch In Electrical Power System

Lecture 13 Case Study - 1 : Economic Load Dispatch In Electrical Power System - Derivation

Lecture 14 Session 13 - Corrections

Lecture 15 MATLAB - Constrained Non-Linear Optimization - Lagrange Multiplier Method

Lecture 16 Python - Constrained Non-Linear Optimization - Lagrange Multiplier Method

Section 5: Unconstrained Non-Linear Optimization - Gradient Descent Algorith

Lecture 17 Single Variable Non-Linear Optimization - Gradient Descent Algorithm

Lecture 18 Numerical- Single Variable Non-Linear Optimization - Gradient Descent Algorithm

Lecture 19 Single Variable Non-Linear Optimization - Gradient Descent Algorithm-MATLAB

Lecture 20 Single Variable Non-Linear Optimization - Gradient Descent Algorithm-Python

Lecture 21 Multi Variable Non-Linear Optimization - Gradient Descent Algorithm

Lecture 22 Multi Variable Non-Linear Optimization - Gradient Descent Algorithm - Numerical

Lecture 23 Multi Variable Non-Linear Optimization - Gradient Descent Algorithm-MATLAB

Lecture 24 Multi-Variable Non-Linear Optimization - Gradient Descent Algorithm-Python

Section 6: Need of Optimization in Machine Learning

Lecture 25 Why we need optimization in machine learning

Are you looking to master optimization techniques and apply them in real-world scenarios using MATLAB and Python? Whether you're a student, engineer, researcher, or data science enthusiast, this course offers a practical and intuitive path to understanding and implementing optimization from the ground up. This course covers everything from the fundamentals of optimization to advanced problem-solving techniques using popular tools like MATLAB and Python. You’ll learn how to mathematically model optimization problems, explore linear and nonlinear optimization, and implement gradient descent algorithms for single and multivariable functions. To make the learning experience even more impactful, this course includes a special lecture on how optimization plays a critical role in machine learning, helping you bridge the gap between theory and real-world applications.