Dr. Rambabu Pemula

  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  

  FIND-S: FINDING A MAXIMALLY SPECIFIC HYPOTHESIS •         To illustrate this algorithm, assume the learner is given the sequence of training examples from the EnjoySport task. Step 1: Initialize h to the most specific hypothesis in H FIND-S: Step-2 ·        The first step of FIND-S is to initialize h to the most specific hypothesis in H h - (Ø, Ø, Ø, Ø, Ø, Ø) ·        Consider the first training example x1 = <Sunny, Warm, Normal, Strong, Warm, Same>, + •         Observing the first training example , it is clear that hypothesis h is too specific. None of the " Ø " constraints in h are satisfied by this example, so each is replaced by the next more general constraint that fits the example h1 = <Sunny, Warm, Normal, Strong, Warm, Same> ·        Consider the second training example x2 = <Sunny, Warm, H...

WEEK 2: • Implement and demonstrate the FIND-S algorithm for finding the most specific hypothesis based on a given set of training data samples. Read the training data from a .CSV file.  FIND - S Algorithm Data Set: