Artificial Intelligence and Machine Learning | Study Materials
| Module | Download Now |
|---|---|
| Module 1 | Click Here |
| Module 2 | Click Here |
| Module 3 | Click Here |
| Module 4 | Click Here |
| Module 5 | Click Here |
This Material/PDF's Covers the Following Topics :
Module-1
What is artificial intelligence?, Problems, problem spaces, and search, Heuristic search techniques
Module-2
Knowledge representation issues, Predicate logic, Representation knowledge using rules. Concept Learning: Concept learning task, Concept learning as search, Find-S algorithm, Candidate Elimination Algorithm, Inductive bias of Candidate Elimination Algorithm.
Module-3
Decision Tree Learning: Introduction, Decision tree representation, Appropriate problems, ID3 algorithm. Artificial Neural Network: Introduction, NN representation, Appropriate problems, Perceptrons, Backpropagation algorithm.
Module-4
Bayesian Learning: Introduction, Bayes theorem, Bayes theorem and concept learning, ML and LS error hypothesis, ML for predicting, MDL principle, Bates optimal classifier, Gibbs algorithm, Navie Bayes classifier, BBN, EM Algorithm
Module-5
Instance-Base Learning: Introduction, k-Nearest Neighbour Learning, Locally weighted regression, Radial basis function, Case-Based reasoning. Reinforcement Learning: Introduction, The learning task, Q-Learning.