ARTIFICIAL INTELLIGENCE & MACHINE LEARNING

21CS1603   ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING 

COURSE OBJECTIVES:

● To understand the various characteristics of intelligent agents

● To learn the different search strategies in AI 

● To learn to represent knowledge in solving AI problems

● To know about the various applications of AI 

● To understand the need for machine learning and various algorithms in machine learning. 

 UNIT I               INTRODUCTION                                                                                        9 

Introduction–Definition – Future of Artificial Intelligence – Characteristics of Intelligent Agents–Typical Intelligent Agents – Problem Solving Approach to Typical AI problemsSearch Strategies- Uninformed – Informed-BFS-Greedy best first search-A* search . 

SUGGESTED ACTIVITIES 

          ●    Developing PEAS description for agents 

          ● Comparing Future of AI 

          ● Different Approach in AI to Real Time Problems 

SUGGESTED EVALUATION METHODS 

        ● Quiz on History of AI 

        ● Learners to write a letter to self-keeping the future in context 

        ● Assignment on Problem Solving Approach 

UNIT II             PROBLEM SOLVING METHODS                                                                 9 

Problem solving Methods – Heuristics - Iterative Deepening A*- RBFS – Memory Bounded A* – Local Search Algorithms and Optimization Problems - Searching with Partial Observations – Constraint Satisfaction Problems – Constraint Propagation – Backtracking Search – Game Playing –Min Max- Optimal Decisions in Games – Alpha Beta Pruning – Stochastic Games 

SUGGESTED ACTIVITIES 

        ● Participating in Game based activity 

        ● Basic Structure of Decision Tree to students

        ● Searching Techniques for Problem Solving 

SUGGESTED EVALUATION METHODS

       ● Designing a decision tree based on the data given 

       ● Quiz on Searching Techniques 

      ● Students are divided into groups to find different solution for a particular problem and it can discussed in class 

UNIT III KNOWLEDGE REPRESENTATION AND AI APPLICATIONS                                                                 9 

First Order Predicate Logic – Prolog Programming – Unification – Forward ChainingBackward Chaining – Resolution – Knowledge Representation – Ontological Engineering- AI applications – Language Models – Information Retrieval- Information Extraction – Natural Language Processing – Machine Translation – Speech Recognition – Robot. 

SUGGESTED ACTIVITIES: 

        ● Installing Prolog.

       ● Game based activity for AI applications. 

       ● Flowchart for Knowledge Representation. 

SUGGESTED EVALUATION METHODS: 

      ● Mystery Animal Game (based on Natural Language Processing). 

      ● Assignment on Retrieval and Extraction techniques. 

      ● Quiz on Simple Prolog Programming. 

UNIT IV MACHINE LEARNING AND SUPERVISED LEARNING ALGORITHMS                                               9 

Introduction to Machine Learning (ML) - Essential concepts of ML –Learning a Class from Examples- Linear, Non-linear-Multi-class and Multi-label classification-Decision Trees- ID3-Classification and Regression Trees (CART)-Regression- Linear RegressionMultiple Linear Regression- Logistic Regression- Bayesian Classifier- Bayesian Network. 

SUGGESTED ACTIVITIES: 

      ● Developing a framework for real life activities such as decision tree. 

      ● Developing algorithms for basic mathematical expressions using regression tree.

      ● Simple program on SVM classification 

SUGGESTED EVALUATION METHODS: 

    ● Quizzes on algorithm and basic python. 

    ● Assignments on illustrative problems. 

    ● Quizzes on simple python programs. 

UNIT V UNSUPERVISED LEARNING AND MACHINE LEARNING APPLICATIONS                                     9 

Introduction to clustering, clustering algorithms - Self-Organizing Map - Expectation Maximization - Gaussian Mixture Models – Principal Component Analysis (PCA) - MACHINE LEARNING APPLICATIONS - Image Recognition – Speech Recognition – Email spam and Malware Filtering – Online fraud detection– Medical Diagnosis. 

SUGGESTED ACTIVITIES: 

    ● Developing a framework for real life activities such as clustering techniques. 

    ● Application of clustering algorithms to datasets (UCI/Kaggle/Corel 10k). 

SUGGESTED EVALUATION METHODS: 

    ● Quizzes on clustering concepts.

    ● Assignments on Machine learning applications. 

TOTAL : 45 PERIODS 

COURSE OUTCOMES 

At the end of the course, the student will be able to 

● Understand concepts of Artificial Intelligence and different types of intelligent agents and their architecture. ● Formulate problems as state space search problem and efficiently solve them.

● Understand the working of various informed and uninformed searching algorithms and different heuristics. ● Understand the concept of knowledge representation.

● Understand supervised and unsupervised learning algorithms. 

● Apply Machine learning algorithms for real world problems. 

TEXT BOOKS 

1. S. Russell and P. Norvig, “Artificial Intelligence: A Modern Approach, Prentice Hall, Fourth Edition, 2020 (Unit 1, 2, 3) 

2. Tom M. Mitchell, “Machine Learning”, Indian Edition, McGraw-Hill, 2017. 

REFERENCE BOOKS 

1. Munesh Chandra Trivedi, “A Classical Approach to Artificial Intelligence”, Khanna Book Publishing, 2019.

2. Vinod Chandra S.S, AnandHareendran S, “Artificial Intelligence and Machine Learning”, PHI Learning, 2014. 

3. David L. Poole and Alan K. Mackworth, ―Artificial Intelligence: Foundations of Computational Agents, Cambridge University Press, 2010. 

4.Charu C. Aggarwal, “Data Classification Algorithms and Applications”, CRC Press, 2014. 

5. Stephen Marsland, “Machine Learning – An Algorithmic Perspective”, 2 nd Edition, CRC Press, 2015.