Autonomous Navigation

The Idea of the Autonomous Artwork

Yale / Literature
Paul H Fry

In this lecture, Professor Paul Fry explores the origins of formalist literary criticism. Considerable attention is paid to the rise and subsequent popularity of the New Critics and their preferred site of literary exploration, the "poem." The idea of autonomous art is explored in the writings of, among others, Kant, Coleridge, and Wilde. Using the work of Wimsatt and Beardsley, the lecture...more

Examples Of Autonomous Linear Dynamical Systems

Stanford / Mathematics
Stephen Boyd

Examples Of Autonomous Linear Dynamical Systems, Finite-State Discrete-Time Markov Chain, Numerical Integration Of Continuous System, High Order Linear Dynamical Systems, Mechanical Systems, Linearization Near Equilibrium Point, Linearization Along Trajectory

Guidance, Navigation and Control

MIT / Engineering
Phil Hattis

An Application of Supervised Learning - Autonomous Deriving

Stanford / Computer Science
Andrew Ng

An Application of Supervised Learning - Autonomous Deriving, ALVINN, Linear Regression, Gradient Descent, Batch Gradient Descent, Stochastic Gradient Descent (Incremental Descent), Matrix Derivative Notation for Deriving Normal Equations, Derivation of Normal Equations

Machine Learning

Stanford / Computer Science
Andrew Ng

This course provides a broad introduction to machine learning and statistical pattern recognition. Topics include: supervised learning (generative/discriminative learning, parametric/non-parametric learning, neural networks, support vector machines); unsupervised learning (clustering, dimensionality reduction, kernel methods); learning theory (bias/variance tradeoffs; VC theory; large margins);

First-order Autonomous ODE's: Qualitative Methods, Applications

MIT / Mathematics
Arthur Mattuck

Non-linear Autonomous Systems: Finding the Critical Points and Sketching Trajectories

MIT / Mathematics
Arthur Mattuck

Least-Squares

Stanford / Engineering
Stephen Boyd

Least-Squares, Geometric Interpretation, Least-Squares (Approximate) Solution, Projection On R(A), Least-Squares Via QR Factorization, Least-Squares Estimation, Blue Property, Navigation From Range Measurements, Least-Squares Data Fitting

Linearization (Continued)

Stanford / Engineering
Stephen Boyd

Linearization (Continued), Navigation By Range Measurement, Broad Categories Of Applications, Matrix Multiplication As Mixture Of Columns, Block Diagram Representation, Linear Algebra Review, Basis And Dimension, Nullspace Of A Matrix

Least-Norm Solution

Stanford / Mathematics
Stephen Boyd

Least-Norm Solution, Least-Norm Solution Via QR Factorization, Derivation Via Langrange Multipliers, Example: Transferring Mass Unit Distance, Relation To Regularized Least-Squares, General Norm Minimization With Equality Constraints, Autonomous Linear Dynamical Systems, Block Diagram

Bayesian Statistics and Regularization

Stanford / Computer Science
Andrew Ng

Bayesian Statistics and Regularization, Online Learning, Advice for Applying Machine Learning Algorithms, Debugging/fixing Learning Algorithms, Diagnostics for Bias & Variance, Optimization Algorithm Diagnostics, Diagnostic Example - Autonomous Helicopter, Error Analysis, Getting Started on a Learning Problem