## PTME3002 Syllabus - Artificial Intelligence And Machine Learning - 2023 Regulation Anna University

PTME3002

ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING

L T P C

3 0 0 3

COURSE OBJECTIVES:
1. Understand the importance, principles, and search methods of AI
2. Provide knowledge on predicate logic and Prolog.
3. Introduce machine learning fundamentals.
4. Study of supervised learning algorithms.
5. Study about unsupervised learning algorithms.

UNIT I

INTELLIGENT AGENT AND UNINFORMED SEARCH

9

Introduction - Foundations of AI - History of AI - The state of the art - Risks and Benefits of AI - Intelligent Agents - Nature of Environment - Structure of Agent - Problem Solving Agents - Formulating Problems - Uninformed Search - Breadth First Search - Dijkstra's algorithm or uniformcost search - Depth First Search - Depth Limited Search

UNIT II

PROBLEM SOLVING WITH SEARCH TECHNIQUES

9

Informed Search - Greedy Best First - A* algorithm - Adversarial Game and Search - Game theory - Optimal decisions in game - Min Max Search algorithm - Alpha-beta pruning - Constraint Satisfaction Problems (CSP) - Examples - Map Coloring - Job Scheduling - Backtracking Search for CSP

UNIT III

LEARNING

9

Machine Learning: Definitions – Classification - Regression - approaches of machine learning models - Types of learning - Probability - Basics - Linear Algebra – Hypothesis space and inductive bias, Evaluation. Training and test sets, cross validation, Concept of over fitting, under fitting, Bias and Variance - Regression: Linear Regression - Logistic Regression

UNIT IV

UPERVISED LEARNING

9

Neural Network: Introduction, Perceptron Networks – Adaline - Back propagation networks - Decision Tree: Entropy – Information gain - Gini Impurity - classification algorithm - Rule based Classification - Naïve Bayesian classification - Support Vector Machines (SVM)

UNIT V

UNSUPERVISED LEARNING

9

Unsupervised Learning – Principle Component Analysis - Neural Network: Fixed Weight Competitive Nets - Kohonen Self-Organizing Feature Maps – Clustering: Definition - Types of Clustering – Hierarchical clustering algorithms – k-means algorithm.

TOTAL:45 PERIODS

OUTCOMES: At the end of the course the students would be able to
1. Understand the foundations of AI and the structure of Intelligent Agents
2. Use appropriate search algorithms for any AI problem
3. Study of learning methods
4. Solving problem using Supervised learning
5. Solving problem using Unsupervised learning

TEXT BOOKS:
1. S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach”, Prentice Hall, Fourth Edition, 2021
2. S.N.Sivanandam and S.N.Deepa, Principles of soft computing-Wiley India.3 rd ed,

REFERENCES:
1. Machine Learning. Tom Mitchell. First Edition, McGraw- Hill, 1997
2. I. Bratko, “Prolog: Programming for Artificial Intelligence‖, Fourth edition, Addison- Wesley Educational Publishers Inc., 2011.
3. C. Muller & Sarah Alpaydin, Ethem. Introduction to machine learning. MIT press, 2020.