Engineering & Robotics

Control theory, dynamical systems, signal processing, convex optimization, robotics, and electrical engineering.

7 courses · 104 standalone talks · 333 total lectures

All Engineering Signal Processing & Robotics (136)Control & Dynamical Systems (113)Complex Systems & Scaling (57)Linear Algebra (46)Convex Optimization (38)Calculus & Analysis (25)Algorithms & Data Structures (23)Computer Architecture (16)Behavioral & Evolutionary Biology (15)science (12)

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Mathematical Optimization & Why Convexity Is the Watershed

Mathematical Optimization & Why Convexity Is the Watershed

EE364a · Lec 1 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:20:32

Lecture 9: The Linear Model II (CS 156)

Lecture 9: The Linear Model II (CS 156)

CS 156 · Lec 9 · Yaser Abu-Mostafa · Caltech

Yaser Abu-Mostafa Caltech

1:27:14

Convex Optimization with Abstract Linear Operators

Convex Optimization with Abstract Linear Operators

Mathematics·Conference Talk · Stephen P. Boyd · Stanford University

Stephen P. Boyd Stanford University

1:04:29

Optimization: A Bootcamp for Machine Learning, Inverse Problems, and Control

Optimization: A Bootcamp for Machine Learning, Inverse Problems, and Control

Engineering·Standalone Lecture · Steve Brunton · University of Washington

Steve Brunton University of Washington

22:28

Leveraging Per-Instance Privacy for Machine Unlearning

Leveraging Per-Instance Privacy for Machine Unlearning

Mathematics·Conference Talk · Anvith Thudi · Google

Anvith Thudi Google

33:23

Convexity 101 [Optimization Bootcamp]

Convexity 101 [Optimization Bootcamp]

Engineering·Standalone Lecture · Steve Brunton · University of Washington

Steve Brunton University of Washington

14:58

Stanford EE364a Lecture 5: The Lagrangian, Dual Function & Slater's Condition

Stanford EE364a Lecture 5: The Lagrangian, Dual Function & Slater's Condition

EE364a · Lec 5 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:16:10

Stanford EE364a Lecture 6: Saddle-Point Interpretation, KKT Conditions & Perturbation

Stanford EE364a Lecture 6: Saddle-Point Interpretation, KKT Conditions & Perturbation

EE364a · Lec 6 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:09:19

Stanford EE364a Lecture 4: Convex Optimization Problems — LP, QP, SOCP & SDP

Stanford EE364a Lecture 4: Convex Optimization Problems — LP, QP, SOCP & SDP

EE364a · Lec 4 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:13:38

Stanford EE364a Lecture 3: Convex Functions — First/Second-Order Conditions & Epigraphs

Stanford EE364a Lecture 3: Convex Functions — First/Second-Order Conditions & Epigraphs

EE364a · Lec 3 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:17:14

Stanford EE364a Lecture 2: Affine Sets, Convex Sets, Cones & Hyperplanes

Stanford EE364a Lecture 2: Affine Sets, Convex Sets, Cones & Hyperplanes

EE364a · Lec 2 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:16:51

Lecture 13: Geometric Problems Continued (EE364a)

Lecture 13: Geometric Problems Continued (EE364a)

EE364a · Lec 13 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:15:17

Lecture 11: Statistical Estimation (EE364a)

Lecture 11: Statistical Estimation (EE364a)

EE364a · Lec 11 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:17:03

4. Wave-Particle Duality of Matter; Schrödinger Equation

4. Wave-Particle Duality of Matter; Schrödinger Equation

5.111SC · Lec 4 · Catherine Drennan · MIT

Catherine Drennan MIT

46:02

3. Wave-Particle Duality of Light

3. Wave-Particle Duality of Light

5.111SC · Lec 3 · Catherine Drennan · MIT

Catherine Drennan MIT

45:51

Lecture 8 | Convex Optimization I (Stanford)

Lecture 8 | Convex Optimization I (Stanford)

EE364a · Lec 8 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:16:30

Lecture 7 | Convex Optimization I

Lecture 7 | Convex Optimization I

EE364a · Lec 7 · Stephen Boyd · Stanford University

Stephen Boyd Stanford University

1:14:37

Computing Lyapunov Functions I (MIT 6.8210/6.832, Lecture 8)

Computing Lyapunov Functions I (MIT 6.8210/6.832, Lecture 8)

6.832 · Lec 8 · Russ Tedrake · MIT

Russ Tedrake MIT

1:23:56

Lecture 12: Introduction to Linear Programming (18.200)

Lecture 12: Introduction to Linear Programming (18.200)

18.200 · Lec 12 · Peter Shor · MIT

1:14:04

Lecture 15: Linear Programming: LP, Reductions, Simplex (6.046J)

Lecture 15: Linear Programming: LP, Reductions, Simplex (6.046J)

6.046J · Lec 17 · Srinivas Devadas · MIT

Srinivas Devadas MIT

1:22:27

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