non-parametric learning
sort by: Relevancy | Title try advanced search for more options
-
Parametric Equations that "move" along the same path.
-
Introduction to parametric equations.
-
The Motivation & Applications of Machine Learning, The Logistics of the Class, The Definition of Machine Learning, The Overview of Supervised Learning, The Overview of Learning Theory, The Overview of Unsupervised Learning, The Overview of Reinforcement Learning
-
The Concept of Underfitting and Overfitting, The Concept of Parametric Algorithms and Non-parametric Algorithms, Locally Weighted Regression, The Probabilistic Interpretation of Linear Regression, The motivation of Logistic Regression, Logistic Regression, Perceptron
-
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
-
Hawkins talks about how he started his first company while he was in college and the lessons he learnt from that experience. Real-world learning along with book and school type learning are instrumental in one's preparation to be an entrepreneur, he says.
-
Eliminating the parameter.
-
Removing the parameter from a more interesting example.
-
Advice for Applying Machine Learning, Debugging Reinforcement Learning (RL) Algorithm, Linear Quadratic Regularization (LQR), Differential Dynamic Programming (DDP), Kalman Filter & Linear Quadratic Gaussian (LQG), Predict/update Steps of Kalman Filter, Linear Quadratic Gaussian (LQG)
-
Musk discusses the role that business school can play for an entrepreneur as well as the possibility of learning outside of school. According to him the, the important principle is to be dedicated to learning what you need to know - whether that be in school or empirically.
-
Learning I
-
Learning II
