CO5 H = Highly Related; M = Medium L = Low References: 1. Digital Image Processing 2ndEdition, Rafael C. Gonzalvez and Richard E. Woods. Published by: Pearson Education. 2. Digital Image Processing and Computer Vision, R.J. Schalkoff. Published by: John Wiley and Sons, NY. 3. Fundamentals of Digital Image Processing, A.K. Jain. Published by Prentice Hall, Upper Saddle River, NJ. BCO 086A MACHINE LEARNING 4-0-0 [4] Course Objectives To understand the basic concepts of learning and decision trees. To understand the neural networks and genetic algorithms To understand the Bayesian techniques To understand the instant based learning To understand the analytical learning and reinforced learning UNIT I INTRODUCTION, CONCEPT LEARNING AND DECISION TREES Learning Problems Designing Learning systems, Perspectives and Issues Concept – – Learning Version Spaces and Candidate Elimination Algorithm Inductive bias – – – Decision Tree learning Representation Algorithm Heuristic Space Search. – – – NEURAL NETWORKS AND GENETIC ALGORITHMS Neural Network UNIT II Representation Problems Perceptrons Multilayer Networks and Back – – – Propagation Algorithms Advanced Topics Genetic Algorithms Hypothesis – – – Space Search Genetic Programming Models of Evolution and Learning. – – BAYESIAN AND COMPUTATIONAL LEARNING Bayes Theorem Concept UNIT III – Learning Maximum Likelihood Minimum Description Length Principle – – – Bayes Optimal Classifier Gibbs Algorithm Naïve Bayes Classifier Bayesian – – – Belief Network EM Algorithm Probably Learning Sample Complexity for – – – Finite and Infinite Hypothesis Spaces Mistake Bound Model. – INSTANT BASED LEARNING AND LEARNING SET OF RULES K- Nearest UNIT IV Neighbor Learning Locally Weighted Regression Radial Basis Functions – – – Case-Based Reasoning Sequential Algorithms Learning Rule Sets – – – Learning First Order Rules Learning Sets of First Order Rules Induction as – – Inverted Deduction Inverting Resolution – UNIT V ANALYTICAL LEARNING AND REINFORCED LEARNING Perfect Domain Theories Explanation Based Learning Inductive-Analytical Approaches - FOCL – – Algorithm Reinforcement Learning Task Q-Learning Temporal Difference – – – – Learning Completion of the course, the students will be able to OUTCOME:On
CO5 H = Highly Related; M = Medium L = Low References: 1. Digital Image Processing 2ndEdition, Rafael C. Gonzalvez and Richard E. Woods. Published by: Pearson Education. 2. Digital Image Processing and Computer Vision, R.J. Schalkoff. Published by: John Wiley and Sons, NY. 3. Fundamentals of Digital Image Processing, A.K. Jain. Published by Prentice Hall, Upper Saddle River, NJ. BCO 086A MACHINE LEARNING 4-0-0 [4] Course Objectives To understand the basic concepts of learning and decision trees. To understand the neural networks and genetic algorithms To understand the Bayesian techniques To understand the instant based learning To understand the analytical learning and reinforced learning UNIT I INTRODUCTION, CONCEPT LEARNING AND DECISION TREES Learning Problems – Designing Learning systems, Perspectives and Issues – Concept Learning – Version Spaces and Candidate Elimination Algorithm – Inductive bias – Decision Tree learning – Representation – Algorithm – Heuristic Space Search. UNIT II NEURAL NETWORKS AND GENETIC ALGORITHMS Neural Network Representation – Problems – Perceptrons – Multilayer Networks and Back Propagation Algorithms – Advanced Topics – Genetic Algorithms – Hypothesis Space Search – Genetic Programming – Models of Evolution and Learning. UNIT III BAYESIAN AND COMPUTATIONAL LEARNING Bayes Theorem – Concept Learning – Maximum Likelihood – Minimum Description Length Principle – Bayes Optimal Classifier – Gibbs Algorithm – Naïve Bayes Classifier – Bayesian Belief Network – EM Algorithm – Probably Learning – Sample Complexity for Finite and Infinite Hypothesis Spaces – Mistake Bound Model. UNIT IV INSTANT BASED LEARNING AND LEARNING SET OF RULES K- Nearest Neighbor Learning – Locally Weighted Regression – Radial Basis Functions – Case-Based Reasoning – Sequential Algorithms – Learning Rule Sets – Learning First Order Rules – Learning Sets of First Order Rules – Induction as Inverted Deduction – Inverting Resolution UNIT V ANALYTICAL LEARNING AND REINFORCED LEARNING Perfect Domain Theories – Explanation Based Learning – Inductive-Analytical Approaches - FOCL Algorithm – Reinforcement Learning – Task – Q-Learning – Temporal Difference Learning OUTCOME:On Completion of the course, the students will be able to