MLIP L16 - Bayesian Classification Part-5 (Decision Surfaces, Discriminant Functions, 1D Gaussian)
Link to the course page for all the relevant material: https://subrahmanyamgorthi.weebly.com/machine-learning-for-image-processing.html In this lecture, an example problem on binary classification is solved using the likelihood function and through the loss matrix method. A detailed explanation of decision surfaces, and discriminant functions are provided. A univariate Gaussian distribution is also introduced. Video Index: 00:00 - Recap of the Last Lecture 02:00 - Example Problem based on the Likelihood 13:50 - Example problem based on the Loss Matrix 23:25 - Decision Surfaces 30:55 - Discriminant Functions 38:35 - Gaussian in 1D & 2D
Link to the course page for all the relevant material: https://subrahmanyamgorthi.weebly.com/machine-learning-for-image-processing.html In this lecture, an example problem on binary classification is solved using the likelihood function and through the loss matrix method. A detailed explanation of decision surfaces, and discriminant functions are provided. A univariate Gaussian distribution is also introduced. Video Index: 00:00 - Recap of the Last Lecture 02:00 - Example Problem based on the Likelihood 13:50 - Example problem based on the Loss Matrix 23:25 - Decision Surfaces 30:55 - Discriminant Functions 38:35 - Gaussian in 1D & 2D