Please read this disclaimer before proceeding This document is confidential and intended solely for the educational purpose of RMK Group of Educational Institutions If you have received this document through email in error please notify the system manager This document contains proprietary information and is intended only to the respective group learning community as intended If you are not the addressee you should not disseminate distribute or copy through e mail Please notify the sender immediately by e mail if you have received this document by mistake and delete this document from your system If you are not the intended recipient you are notified that disclosing copying distributing or taking any action in reliance on the contents of this information is strictly prohibited 22IT401 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING Department Information Technology Batch Year 2022 2026 II Created by Dr T Mahalingam Dr S Selvakanmani Date 12 01 2024 S NO CONTENTS SLIDE NO Table of Contents CONTENTS COURSE OBJECTIVES PRE REQUISITES COURSE NAMES WITH CODE SYLLABUS WITH SUBJECT CODE NAME LTPC DETAILS 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 ACTIVITY BASED LEARNING UNIT 1 COURSE OUTCOMES 6 CO PO PSO MAPPING LECTURE PLAN UNIT 1 CROSSWORD PUZZLE VIDEO LINK QUIZ TEST YOURSELF LECTURE NOTES UNIT 1 ASSIGNMENT 1 UNIT 1 PART A Q A WITH K LEVEL AND CO PART B Q s WITH K LEVEL AND CO SUPPORTIVE ONLINE CERTIFICATION COURSES REAL TIME APPLICATIONS IN DAY TO DAY LIFE AND TO INDUSTRY CONTENTS BEYOND THE SYLLABUS ASSESSMENT SCHEDULE PRESCRIBED TEXT BOOKS REFERENCE BOOKS MINI PROJECT SUGGESTIONS 5 6 7 8 12 13 15 16 17 18 19 20 68 70 76 77 79 80 82 83 84 2 COURSE OBJECTIVES Understand the concept of agents problem solving and searching strategies Familiarize with Knowledge reasoning and representation based AI systems and approaches Apply the aspect of Probabilistic approach to AI Understanding of concepts of machine learning approaches Recognize the concepts of Machine Learning and its deterministic tools 3 PRE REQUISITES PRE REQUISITE CHART E C N E G I L L E T N I I L A C I F I T R A 1 0 4 T I 2 2 I G N N R A E L E N H C A M D N A I 22MA401 Probability and Statistics 22CS303 Design and Analysis of Algorithms 4 22IT401 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING L T P C 3 0 0 3 OBJECTIVES Understand the concept of Artificial Intelligence Familiarize with Knowledge based AI systems and approaches Apply the aspect of Probabilistic approach to AI Identify the Neural Networks and NLP in designing AI models Recognize the concepts of Machine Learning and its deterministic tools UNIT 1 PROBLEM SOLVING AND SEARCH STARTEGIES Introduction What Is Ai The Foundations Of Artificial Intelligence The History Of Artificial Intelligence The State Of The Art Intelligent Agents Agents And Environments Good Behaviour The Concept Of Rationality The Nature Of Environments And The Structure Of Agents Solving Problems By Searching Problem Solving Agents Uninformed Search Strategies Informed Heuristic Search Strategies Heuristic Functions Beyond Classical Search Local Search Algorithms and Optimization Problems Searching With Nondeterministic Actions And Partial Observations Online Search Agents And Unknown Environments Constraint Satisfaction Problems Definition Constraint Propagation Backtracking Search Local Search The Structure Of Problems List of Exercise Experiments 1 Implementation of uninformed search algorithm BFS and DFS 2 Implementation of Informed Search algorithm A and Hill Climbing Algorithm UNIT 2 KNOWLEDGE REPRESENTATION AND REASONING Logical Agents Knowledge Based Agents Propositional Logic Propositional Theorem Proving Effective Propositional Model Checking Agents Based on Propositional Logic FirstOrder Logic Syntax and Semantics Knowledge Engineering in FOL Inference in First Order Logic Unification and Lifting Forward Chaining Backward Chaining Planning Definition Algorithms Planning Graphs Hierarchical Planning Multi agent Planning Knowledge Representation Ontological Engineering Categories and Objects Events Mental Events and Mental Objects Reasoning Systems for Categories Reasoning with Default Information The Internet Shopping World List of Exercise Experiments 1 Implementation of forward and backward chaining 2 Implementation of unification algorithms 8 4 22IT401 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING UNIT 3 LEARNING Learning from Examples Forms of Learning Supervised Learning Learning Decision Trees Evaluating and Choosing the Best Hypothesis The Theory of Learning Regression and Classification with Linear Models Artificial Neural Networks Applications Human computer interaction HCI Knowledge management technologies AI for customer relationship management Expert systems Data mining text mining and Web mining L T P C 3 0 0 3 Other current topics List of Exercise Experiments 1 Numpy Operations 2 NumPy arrays 3 NumPy Indexing and Selection 4 NumPy Exercise i Write code to create a 4x3 matrix with values ranging from 2 to 13 ii iii Write code to replace the odd numbers by 1 in the following array Perform the following operations on an array of mobile phones prices 6999 7500 11999 27899 14999 9999 a Create a 1d array of mobile phones prices b Convert this array to float type c Append a new mobile having price of 13999 Rs to this array d Reverse this array of mobile phones prices e Apply GST of 18 on mobile phones prices and update this array f Sort the array in descending order of price g What is the average mobile phone price TOTAL 45 PERIODS 9 4 22IT401 ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING L T P C 3 0 0 3 UNIT 4 FUNDAMENTALS OF MACHINE LEARNING Motivation for Machine Learning Applications Machine Learning Learning associations Classification Regression The Origin of machine learning Uses and abuses of machine learning Success cases How do machines learn Abstraction and knowledge representation Generalization Factors to be considered Assessing the success of learning Metrics for evaluation of classification method Steps to apply machine learning to data Machine learning process Input data and ML algorithm Classification of machine learning algorithms General ML architecture Group of algorithms Reinforcement learning Supervised learning Unsupervised learning Semi Supervised learning Algorithms Ensemble learning Matching data to an appropriate algorithm List of Exercise Experiments available Google colabs Data and Sentiment Analysis 1 Build linear regression models to predict