Module 1 1] Define Computer Graphics. Explain the applications of Computer Graphics. 2] Describe the basic structure of an OpenGL graphics program, including necessary OpenGL functions. 3] Explain Bresenham’s Line drawing algorithm with…
Difference Between Classification and Clustering S.No Classification Clustering 1. Supervised learning Unsupervised learning 2. Works with labelled data Works with unlabelled data 3. Requires prior knowledge or domain expertise No…
Applications of Artificial Neural Networks Artificial Neural Networks (ANNs) are widely used in various real-world domains due to their ability to handle complex, nonlinear, and noisy data. 1. Real-Time Applications:…
Structure of Perceptron Model: A perceptron consists of: Mathematical Model:
Artificial neurons are the basic units of an Artificial Neural Network (ANN). They are inspired by the biological neurons in the human brain and function similarly by receiving inputs, processing…
The Bayes Optimal Classifier is a probabilistic model that uses Bayes theorem to make the most accurate classification of a new instance by considering the predictions from all possible hypotheses,…
The Naive Bayes algorithm is a supervised classification algorithm used for binary or multi-class classification. It works on the Bayes theorem principle and assumes that all features in the dataset…
Fundamentals of Bayes’ Theorem Bayes’ theorem uses three types of probabilities: Bayes Theorem: Classification Using Bayes Model
A regression tree is a type of decision tree used when the target attribute is continuous (not categorical). Instead of classifying into categories, the goal is to predict a real…
C4.5 is an improved version of ID3. It addresses the limitations of ID3 and enhances its capabilities. Features of C4.5: Algorithm : C4.5 Decision Tree Construction Input: Training dataset TOutput:…
