Gorthi Subrahmanyam
I am Dr. Subrahmanyam Gorthi, faculty in the Department of Electrical Engineering, Indian Institute of Technology (IIT) - Tirupati, India. You may refer to my website: https://subrahmanyamgorthi.weebly.com/ for more details. I hope you will find some useful content on this channel!
Overview of My Academic and Research Activities
MLIP L37 Principal Component Analysis (PCA)
MLIP L36 SVM for Nonlinear Classification and Linear Regression
MLIP L35 SVM for Linear Nonseparable and Nonlinear Classification
MLIP L34 SVM for Linear Separable Classification
MLIP L33 Introduction to Support Vector Machines (SVM)
MLIP L32 General Guidelines about Implementation of Neural Networks
MLIP L31 - Backpropagation Part-2
MLIP L30 - Backpropagation Part-1
MLIP L29 - Nonlinear Classifier Part-2
MLIP L28 - Nonlinear Classifier Part-1
MLIP L27 - Linear Classifier Part-2
MLIP L26 - Linear Classifier Part-1
MLIP L25 - Bayesian Classification Part-13 (Maximum a Posteriori Probability (MAP) Estimation)
MLIP L24 - Bayesian Classification Part-12 (Maximum Likelihood Parameter Estimation Part-2)
MLIP L23 - Discussion of the Midterm Exam Paper
MLIP L22 - Bayesian Classification Part-11 (Recap, Maximum Likelihood (ML) Parameter Estimation)
L06 - Image Processing Lab - Bayesian Classifier Basics
MLIP L21 - Bayesian Classification Part-10 (Decision planes, Minimum Distance Classifier, Examples)
MLIP L18 - Bayesian Classification Part-7 (Normally Distributed Classes, 2D Discriminant Functions)
MLIP L20 - Bayesian Classification Part-9 (Decision Hyper Planes for Different Covariance Matrices)
MLIP L19 - Bayesian Classification Part-8 (Illustration of Discriminant Functions & Decision Planes)
MLIP L17 - Bayesian Classification Part-6 (Multivariate Gaussian Distribution, Isocurves, Examples)
MLIP L16 - Bayesian Classification Part-5 (Decision Surfaces, Discriminant Functions, 1D Gaussian)
MLIP L15 - Bayesian Classification Part-4 (Min. Average Risk, Likelihood Ratio-based Decision Rule)
L05 - Image Processing Lab - Canny Edge Detector
MLIP L14 - Bayesian Classification Part-3 (Decision Error, Average Risk, Generalizing to M Classes)
MLIP L13 - Bayesian Classification Part-2 (Bayes Theorem, A posteriori Probability, Likelihood)
MLIP L12 - Bayesian Classification Part-1 (Overview of Machine Learning, Classifier, Feature Vector)
MLIP L11 - Image Processing: Part-9 (Kernels for Gradients, Edge Detection, Derivatives, Sharpening)