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Mathematical Foundations of Machine Learning →0/7
1

The Geometry of Linear Equations

Gilbert Strang · 39:49

2

Transposes, Permutations & Vector Spaces R^n

Gilbert Strang · 47:41

3

Convex Functions — First/Second-Order Conditions & Epigraphs

Stephen Boyd · 1:17:14

4

Saddle-Point Interpretation, KKT Conditions & Perturbation

Stephen Boyd · 1:09:19

5

The Learning Problem

Yaser Abu-Mostafa · 1:21:27

6

Is Learning Feasible?

Yaser Abu-Mostafa · 1:16:48

7

Building makemore Part 3: Activations, Gradients, Kaiming Init & BatchNorm

Andrej Karpathy · 1:55:57

Convex Functions — First/Second-Order Conditions & Epigraphs

Stephen BoydDistinguishedStanford University

Samsung Professor in the School of Engineering

1:17:14·2008·graduate level
Path step 3: Connects first- and second-order curvature conditions to global convexity.

Covers Jensen's inequality, Hessian positive semidefiniteness ∇²f(x) ⪰ 0, log-sum-exp, matrix-fractional functions, and pointwise supremum rules.

EngineeringConvex OptimizationSignal Processing & RoboticsConvex FunctionsHessian

Professor Stephen Boyd, of the Stanford University Electrical Engineering department, lectures on convex and concave functions for the course, Convex Optimization I (EE 364A).

Source: Stanford via YouTubeWatch on YouTube
LecturerAll lecturers →
Stephen BoydDistinguished

Samsung Professor of Engineering, Stanford & Co-Author of Convex Optimization

Samsung Professor of Engineering at Stanford whose textbook and EE364a lectures established convex optimization as a core language across control, signal processing, and ML.

2 courses · 43 lecturesCanon
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Transposes, Permutations & Vector Spaces R^n

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Saddle-Point Interpretation, KKT Conditions & Perturbation

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