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 – 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...

Machine Learning  Introduction Concept Learning and the General to Specific Ordering Decision Tree Learning Artificial Neural Networks Bayesian Learning Instance-Based Learning Genetic Algorithms Learning Sets of Rules Analytical Learning Reinforcement Learning 👉 Machine Learning 2 Syllabus UNIT-1 :  Introduction &  Concept Learning and the General to Specific Ordering Introduction - Well-Posed Learning Problems, Designing a Learning System, Perspectives and Issues in Machine Learning, Introduction to Supervised, Unsupervised and Reinforcement Learning. Concept Learning and the General to Specific Ordering – Introduction, A Concept Learning Task, Concept Learning as Search, Find-S: Finding a Maximally Specific Hypothesis, Version Spaces and the Candidate Elimination Algorithm. 👉 Machine Learning 2- UNIT-1 (A) Notes: Introduction & Concept Learning and the General to Specific Ordering Notes 👉 Machine Learning 2 UNIT-1 (A) PPTs: Introduction PPTs 👉 Machine L...

  CONCEPT LEARNING AS SEARCH ·        Concept learning can be viewed as ·        the task of searching through a large space of hypotheses implicitly defined by the hypothesis representation. ·        The goal of this search is to ·        find the hypothesis that best fits the training examples . Example: •         Consider the instances X and hypotheses H in the EnjoySport learning task. •         The attribute •         Sky has three possible values , and •         AirTemp , Humidity , Wind, Water, Forecast each have two possible values , •         the instance space X contains •         exactly 3*2*2*2*2*2 = 96 distinct inst...