Machine Learning

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 Learning 2- UNI

  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 instances •         5*4*4*4*4*4 = 5120 syntactically distinct hypotheses within H . •         Every hypothesis containing one or more "Φ" symbols represents the empty set of instances ; that is, it classifies every instance as negative . •         1 + (4*3*3*3*3*3) = 973 Semantically distinct hypotheses . A CONCEPT LEAR

  CONCEPT LEARNING ·        Learning involves acquiring general concepts from specific training examples . ·        Example: People continually learn ·        general concepts or ·        categories such as ü   " bird," ü   "car," ü   "situations in which I should study more in order to pass the exam," etc. ·        Each such concept can be viewed as ·        describing some subset of objects or events ü   defined over a larger set.   ·        Alternatively, each concept can be thought of as a Boolean-valued function defined over this larger set. ·        Example: A function defined over all animals , whose value is ·        true for birds and ·        false for other animals .   Definition: Concept learning - Inferring a Boolean-valued function from training examples of its input and output What is Concept Learning…? •         “A Task of acquiring a potential hypothesis ( Solution ) that best fits the gi

  5. CONCEPT LEARNING ·        Learning involves acquiring general concepts from specific training examples. Example: People continually learn general concepts or categories such as "bird," "car," "situations in which I should study more to pass the exam," etc. ·        Each such concept can be viewed as describing some subset of objects or events defined over a larger set ·        Alternatively, each concept can be thought of as a Boolean-valued function defined over this larger set. (Example: A function defined over all animals, whose value is true for birds and false for other animals). Definition: Concept learning - Inferring a Boolean-valued function from training examples of its input and output