Optimization and Convexity: ML and Operations Foundations
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From gradients to convex duality—toolkit for machine learning and decision making.
10 content steps
4 quizzes
~210 minutes
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Journey Steps
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1
Content Step 1
Content
Unconstrained optimization and critical points
2
Content Step 2
Content
Gradient descent and variants (intuition)
3
Content Step 3
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Convex sets, convex functions, and Jensen’s inequality
4
Quiz 4
Quiz
Checkpoint Quiz
5
Content Step 5
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Optimality conditions and KKT (overview)
6
Content Step 6
Content
Linear programming and the simplex method
7
Content Step 7
Content
Duality and sensitivity analysis
8
Quiz 8
Quiz
Checkpoint Quiz
9
Content Step 9
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Quadratic programming and regularization links
10
Content Step 10
Content
Projected and proximal gradient (conceptual)
11
Content Step 11
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Stochastic optimization and convergence (intuitive)
12
Quiz 12
Quiz
Checkpoint Quiz
13
Content Step 13
Content
Applications: ML training, portfolios, resource allocation
14
Quiz 14
Quiz
Checkpoint Quiz
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