Bsc CSIT Semester 4 – Artificial Intelligence – Unit V: Machine Learning (9 Hrs.)

1. Relation Between Biological and Artificial Neural Networks | Biological Element | Role | Artificial Neural Network Equivalent | |-------------------|------|-------------------------------------| |

Reinforcement Learning & ANN OR Gate 1. What is Reinforcement Learning? Reinforcement Learning (RL) is a type of machine learning in which an agent learns by interacting with an environment. The ag

Reinforcement Learning & ANN OR Gate 1. What is Reinforcement Learning? Reinforcement Learning (RL) is a type of machine learning in which an agent learns by interacting with an environment. The ag

Supervised vs Unsupervised Learning 1. Supervised Learning - Data comes with input–output pairs (labeled data). - The model learns a mapping from X → Y. - Used for classification (spam/ham) or regres

How Genetic Algorithm (GA) Works A Genetic Algorithm (GA) is an optimization technique inspired by natural selection and evolution. It searches for the best solution by improving a population of can

What is Fuzzy Logic? Fuzzy Logic is a form of reasoning that deals with degrees of truth rather than strict true/false values. In classical logic, a statement is either 0 or 1, but in fuzzy logic it

Example of Reinforcement Learning (RL) Reinforcement Learning is a learning method where an agent learns by interacting with the environment and receiving rewards or penalties. Example: Robot Learnin

1. Mathematical Model of a Neural Network A neural network neuron performs three main operations: (a) Weighted Sum $$ net = \sum{i=1}^{n} wi xi + b $$ (b) Activation Function $$ y = f(net) $$ (c) F

Crossover Operation in Genetic Algorithm Crossover is a genetic operator used to combine the genetic information of two parents to generate new offspring. It simulates sexual reproduction in nature.

Posterior Probability Posterior probability is the probability of an event occurring given some evidence. It is computed using Bayes’ Theorem: $$ P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)} $$ Where:

1. Mathematical Model of Artificial Neural Network (ANN) An ANN is composed of neurons that process information using inputs, weights, bias, and an activation function. Neuron Model (a) Weighted Sum

Genetic Algorithm Learning (Genetic Approach) Genetic Algorithm (GA) is an optimization and learning technique inspired by natural evolution. It uses selection, crossover, and mutation to evolve bet

Supervised vs Unsupervised Learning and Naïve Bayes 1. Supervised Learning - Definition: Learning from a labeled dataset, where the input-output mapping is known. - Goal: Predict output (label) for

Genetic Algorithm (GA) 1. Definition - GA is an optimization/search technique inspired by natural evolution. - Works by evolving a population of solutions using selection, crossover, and mutation.