Concepts in Machine Learning-CST 383 KTU-Minor Notes-Dr Binu V P

  Learn basics of probability from here  https://mmlandpython.blogspot.com/2021/09/40-basics-of-probability.html Bayesian Formulation We live in a probabilistic world. Everything that happens has uncertainty attached to it. The Bayesian interpretation of probability is what Machine Learning is based upon. Bayesian probability means that we think of probability as quantifying the uncertainty of an event. Because of this, we have to base our probabilities on the information available about an event, rather than counting the number of repeated trials. For example, when predicting a football match, instead of counting the total amount of times Manchester United have won against Liverpool, a Bayesian approach would use relevant information such as the current form, league placing and starting team. The benefit of taking this approach is that probabilities can still be assigned to rare events, as the decision making process is based on relevant features and reasoning. Thomas Bayes, founde

Watch this video before you start reading  SVM A Support Vector Machine (SVM) classifier is a powerful supervised machine learning algorithm used for classification and regression tasks. It is particularly well-suited for binary classification problems but can be extended to multi-class classification as well. It can easily handle multiple continuous and categorical variables. SVM constructs a hyperplane in multidimensional space to separate different classes. SVM generates optimal hyperplane in an iterative manner, which is used to minimize an error. The core idea of SVM is to find a maximum marginal hyperplane(MMH) that best divides the dataset into classes. Support Vectors Support vectors are the data points, which are closest to the hyperplane. These points will define the separating line better by calculating margins. These points are more relevant to the construction of the classifier. Hyperplane A hyperplane is a decision plane which separates between a set of objects having

Neural Networks (ANN - Artificial Neural Network) Introduction The term "Artificial Neural Network" is derived from Biological neural networks that develop the structure of a human brain. Similar to the human brain that has neurons interconnected to one another, artificial neural networks also have neurons that are interconnected to one another in various layers of the networks. These neurons are known as nodes. An Artificial Neural Network in the field of Artificial intelligence where it attempts to mimic the network of neurons makes up a human brain so that computers will have an option to understand things and make decisions in a human-like manner. The artificial neural network is designed by programming computers to behave simply like interconnected brain cells. There are around 1000 billion neurons in the human brain. Each neuron has an association point somewhere in the range of 1,000 and 100,000. In the human brain, data is stored in such a manner as to be distribut