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• Calculus • Linear Algebra • Probability & Statistics • Information Theory • Optimization in Machine Learning

11.1 Review of Differential Calculus 11.2 Introductory Concepts 11.3 Convex and Concave Functions 11.4 Solving NLPs with One Variable 11.5 Golden Section Search 11.6 Unconstrained Maximization and Minimization with Several Variables 11.7 The Method of Steepest Ascent 11.8 Lagrange Multipliers 11.9 The Kuhn-Tucker Conditions 11.10 Quadratic Programming 11.11 Separable Programming 11.12 The Method of Feasible Directions 11.13 Pareto Optimality and Trade Off Curves

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