21AI71 Advanced Ai And Ml

Course Learning Objectives

CLO 1. Demonstrate the fundamentals of Intelligent Agents
CLO 2. Illustrate the reasoning on Uncertain Knowledge
CLO 3. Explore the explanation-based learning in solving AI problems
CLO 4. Illustrate the use of KNN
CLO 5. Explore the Text feature Engineering concepts with Applications

SYLLABUS COPY

MODULE - 1

Intelligent Agents Agents and Environments, Good Behaviour: The Concept of Rationality, The Nature of
Environments, The Structure of Agents

Problem Solving : Game Paying

MODULE - 2

Uncertain knowledge and Reasoning: Quantifying Uncertainty, Acting under Uncertainty , Basic Probability
Notation, Inference Using Full Joint Distributions, Independence , Bayes’ Rule and Its Use The WumpusWorld
Revisited,

MODULE - 3

Neural Network Representation – Problems – Perceptrons – Multilayer Networks and Back Propagation
Algorithms – Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evolution and Learning.

Neural networks and genetic algorithms
Brief history and Evolution of Neural network, Biological neuron, Basics of ANN, Activation function, MP
model.

MODULE - 4

Recommender System
Datasets, Association rules, Collaborative filtering, User-based similarity, item-based similarity, using
surprise library, Matrix factorization

Text Analytics
Overview, Sentiment Classification, Naïve Bayes model for sentiment classification, using TF-IDF vectorizer,
Challenges of text analytics

MODULE - 5

Clustering
Introduction, Types of clustering, Partitioning methods of clustering (k-means, k-medoids), hierarchical
methods

Instance Based Learning: Introduction, k-nearest neighbour learning(review), locally weighted regression,
radial basis function, cased-based reasoning,

Course outcome

At the end of the course the student will be able to:
CO 1. Demonstrate the fundamentals of Intelligent Agents
CO 2. Illustrate the reasoning on Uncertain Knowledge
CO 3. Explore the explanation-based learning in solving AI problems
CO 4. Apply effectively ML algorithms to solve real world problems.
CO 5. Apply Instant based techniques and derive effectively learning rules to real world problems.

Suggested Learning Resources

Textbooks
1. Artificial Intelligence, A Modern Approach, Stuart J. Russell and Peter Norvig, Third Edition, Pearson,
2010
2. Tom M. Mitchell, Machine Learning, McGraw-Hill Education, 2013
3. Machine Learning, Anuradha Srinivasaraghavan, VincyJoeph, Wiley 2019
4. Machine Learning using Python ,Manaranjan Pradhan, U Dinesh Kumar, Wiley 2019

Reference
1. An Introduction to Multi Agent Systems, Michael Wooldridge, Second Edition, John Wiley & Sons