Machine Learning

UNIT-1 (A) Introduction   Introduction: Well-Posed Learning Problems Designing a Learning System Perspectives and Issues in Machine Learning Introduction to Supervised Unsupervised and Reinforcement Learning  

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 

  3. PERSPECTIVES AND ISSUES IN MACHINE LEARNING Issues in Machine Learning ·        What algorithms exist for learning general target functions from specific training examples? In what settings will particular algorithms converge to the desired function, given sufficient training data? Which algorithms perform best for which types of problems and representations? ·        How much training data is sufficient? What general bounds can be found to relate the confidence in learned hypotheses to the amount of training experience and the character of the learner's hypothesis space? ·        When and how can prior knowledge held by the learner guide the process of generalizing from examples? Can prior knowledge be helpful even when it is only approximately correct? ·        What is the best strategy for choosing a useful next training experience, and how does the choice of this strategy alter the complexity of the learning problem? ·        What is the best way to reduce the