Explore Foundations of Optimization

Skill Outline

1. 1. Introduction (3:06)
2. 2. Optimization in Machine Learning (8:18)
3. 3. Decision Variables, Objective Functions, and Constraints (5:26)
4. 4. Feasibility Regions (9:03)
5. 5. Constrained and Unconstrained Optimization (7:53)
6. 6. Gradient Descent Vs Stochastic Gradient Descent (5:54)
7. 7. Challenge 🎉 (0:06)
8. 8. Imagine you are analyzing a food delivery startup. You are tasked with minimizing the total deliver time across all city zones. You can assign up to 10 delivery robots. (0:37)
9. 9. A small startup sells two SaaS tiers: Basic (B) and Pro (P). Each basic plan earns $50, and each Pro plan earns $100. Support team can handle up to 300 customers, and the Pro team can handle at most 120 Pro clients. (0:40)
10. 10. In this scenario, you are minimizing a loss function for a regression model using gradient descent. Your dataset has 10 million rows, and each iteration is taking forever to complete—way too long. (0:38)