Dr. Rambabu Pemula

  FUNDAMENTALS OF MACHINE LEARNING Unit – I Introduction andConcept Learning: Well-posed learning problems, designing a learning system Perspectives and issues in machine learning, Types of learning.  Concept learning task , Concept Learning as search through a hypothesis space, Finding maximally specific hypotheses, Version spaces and the candidate elimination algorithm, Inductive Bias 👉 UNIT-1 (A) Introduction Notes   👉 UNIT-1(B) Concept Learning PPTs 👉 UNIT-1 Introduction and Concept Learning Notes 👉 UNIT-1 Introduction and Concept Learning Long Answer Question Unit - II Decision Treelearning and Neural Network learning : Decision Tree representation and learning algorithm, appropriate problems for Decision Tree Learning, Hypothesis space search in Decision Tree Learning, Inductive bias in Decision Tree Learning: Occam’s razor, Issues in Decision Tree Learning.  Introduction to Neural Network Learning , Introduction to Perceptions, Perceptions, Introductio...

  Unit – V: Genetic Algorithm Genetic Algorithm : 1.      Biological Motivation 2.      Representing Hypothesis 3.      Genetic Operators 4.      Fitness Function and Selection 5.      Hypothesis Space Search 6.      Genetic Programming 7.      Models of Evolution and Learning 8.      Parallelizing Genetic Algorithms

  Unit – IV: Instance-Based Techniques Instance-based Techniques : 1.      Introduction to Instance-Based Techniques 2.      Lazy vs. Eager Generalization 3.      K-Nearest Neighbor 4.      Radial Basis Function 5.      Case Based Reasoning 6.      Remarks on Lazy vs Eager Learning

  Unit – III: Support Vector Machine- Bayesian Learning Support Vector Machines: 1.      Introduction 2.      Linear Classifier 3.      Non-linear Classifier 4.      Training SVM 5.      Support Vector Regression Bayesian Learning : 1.      Bayes theorem and concept learning 2.      Minimum Description Length Principle 3.      Bayes optimal classifier 4.      Gibbs Algorithm 5.      Naïve Bayes Classifier 6.      The EM algorithm 👉 UNIT-3 (A) Support Vector Machine Notes 👉 UNIT-3 (A) Support Vector Machine PPTs 👉 SVM Program 👉 Training SVM 👉 Extra Info on SVM    

  Unit – II: Decision Tree learning and Neural Network learning Decision Tree learning 1.      Decision Tree representation and learning algorithm, 2.      appropriate problems for Decision Tree Learning, 3.      Hypothesis space search in Decision Tree Learning, 4.      Inductive bias in Decision Tree Learning: Occam’s razor, 5.      Issues in Decision Tree Learning. Introduction to Neural Network Learning , 1.      Introduction to Perceptions, 2.      Perceptions, 3.      Introduction to Gradient Descent 4.      Back propagation. 👉 UNIT-2 (A) Decision Tree Learning Notes   👉 UNIT-2 (A) Decision Tree Learning PPTs 👉 UNIT-2(A) Decision Tree Learning Long Answer Questions 👉 UNIT-2 (B) Introduction to Neural Network Learning Notes 👉 UNIT-2 (B) Introduction to Neural Network Learnin...

  Unit – I: Introduction and Concept Learning Introduction 1.      Well-posed learning problems, 2.      D esigning a learning system 3.      Perspectives issues in machine learning, 4.      Types of learning. Concept Learning 5.      Concept learning task, 6.      Concept Learning as search through a hypothesis space, 7.      Finding maximally specific hypotheses (FIND-S) 8.      Version spaces and the candidate elimination algorithm, 9.      Inductive Bias 👉 UNIT-1 (A) Introduction Notes   👉 UNIT-1(B) Concept Learning PPTs 👉 UNIT-1 Introduction and Concept Learning Notes 👉 UNIT-1 Introduction and Concept Learning Long Answer Question  

  FUNDAMENTALS OF MACHINE LEARNING (Professional Elective - 3) Department : IT IV B.Tech I Semester Course Code Hours/Week Credits Marks A2275E9 L T P C CIE SEE Total 3 0 0 3 40 60 100   Course Outcomes: At the end of the course, the student should be able to 1.      Ability to identify machine learning problems corresponding to different applications . 2.      Ability to understand the basic concepts such as Decision trees and Neural Networks. 3.      Apply Machine Learning algorithms to solve problems of moderate complexity. 4.      Identify Machine Learning applications 5.      Understand Genetic algorithms and their applications Unit – I Introduc...