FOUNDAMENTALS IN AI and ML

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About Course

Course Objectives:
 To understand the various characteristics of Intelligent agents
 To learn about the different search strategies in AI
 To learn to represent knowledge in solving AI problems
 To understand the different ways of designing software agents and Prolog
 To learn the Machine Learning Techniques

Course Content

UNIT I INTRODUCTION
Introduction–Definition - Future of Artificial Intelligence Characteristics of Intelligent Agents - Typical Intelligent Agents – Problem Solving Approach to Typical AI problems.

UNIT II PROBLEM SOLVING METHODS
Problem solving Methods - Search Strategies- Uninformed - Informed - Heuristics - Local Search Algorithms and Optimization Problems - Searching with Partial Observations – Constraint Satisfaction Problems – Constraint Propagation - Backtracking Search - Game Playing – Optimal Decisions in Games – Alpha - Beta Pruning - Stochastic Games

UNIT III KNOWLEDGE REPRESENTATION
First Order Predicate Logic – Prolog Programming – Unification Forward Chaining-Backward Chaining – Resolution – Knowledge Representation - Ontological Engineering-Categories and Objects

UNIT IV SOFTWARE AGENTS and PROLOG
Architecture for Intelligent Agents – Agent communication – Negotiation and Bargaining – Argumentation among Agents – Trust and Reputation in Multi-agent systems. Facts and predicates, data types, goal finding, backtracking, simple object, compound objects, use of cut and fail predicates, recursion, lists, simple input/output, dynamic database.

UNIT V INTRODUCTION TO MACHINE LEARNING
Learning – Types of Machine Learning – Supervised Learning – Reinforcement Learning - The Brain and the Neuron – Design a Learning System – Perspectives and Issues in Machine Learning – Concept Learning Task – Concept Learning as Search – Finding a Maximally Specific Hypothesis –Version Spaces and the Candidate Elimination Algorithm – Linear Discriminants – Perceptron – Linear Separability – Linear Regression.

UNIT V INTRODUCTION TO MACHINE LEARNING
Learning – Types of Machine Learning – Supervised Learning – Reinforcement Learning - The Brain and the Neuron – Design a Learning System – Perspectives and Issues in Machine Learning – Concept Learning Task – Concept Learning as Search – Finding a Maximally Specific Hypothesis –Version Spaces and the Candidate Elimination Algorithm – Linear Discriminants – Perceptron – Linear Separability – Linear Regression.

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