3 Review(s)

1.To provide Knowledge on Probability distributions2.To study the algebra and its uses in solving Eigen value problems and single value decomposition problems 3.To explain the concepts in number theory 4.To predict a probability distribution over a set of classes given an observation of input instead of simply outputting the most likely class to which the observation should belong 5.The objective of a simple regression course is to teach students the fundamental concepts and techniques of regression analysis focusing on models with one independent variable 6.This includes understanding the assumption of regression interpreting regression coefficient assessing model fit making predictions and using regression for inference and hypothesis testing 7.Support Vector Machine Terminology 8.Unsupervised Machine Learning and Divisive clustering 9.EM Algorithm in Machine Learning ( Expectation Maximization)10.Emphasizes Decision Trees with Pre Pruning and Post Pruning Techniques Random Forest and its Applications11.Explore various types of perceptron and Neural Networks Details Convolutional Neural Networks with Step by Implementation 12.Examines Recurrent Neural networks including their Types Architectures and Applications 13.Delves into K Nearest Neighbors 14.Learning Locally Weighted Regression Learning and Radio Basis Function