Machine Learning

UNIT-1 (B)   Concept Learning and the General to Specific Ordering   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  

  A CONCEPT LEARNING TASK •         Consider the example task of learning the target concept " Days on which Tom enjoys his favourite water sport ” Table : Positive and negative training examples for the target concept EnjoySport .   ü   The task is to learn to predict the value of EnjoySport for an arbitrary day , based on the values of its other attributes? ü   What hypothesis representation is provided to the learner?   ·        Let’s consider a simple representation in which each hypothesis consists of a conjunction of constraints on the instance attributes . ·        Let each hypothesis be a vector of six constraints , specifying the values of the six attributes ·        Sky ·        AirTemp ·        Humidity ·        Wind ·        Water ·        Forecast •         For each attribute , the hypothesis will either ü   Indicate by a "?' that any value is acceptable for this attribute , ü   Specify a single required value (e.g., Warm ) for the attribute , or ü   I

  What is Machine Learning •         “ Machine Learning •         enables a Machine to Automatically learn from Data , •         improve performance from an Experience and •         predict things without explicitly programmed .” •         In Simple Words, when we fed the Training Data to Machine Learning Algorithm, ü   this algorithm will produce a mathematical model and ü   with the help of the mathematical model , ü   the machine will make a prediction and ü   take a decision without being explicitly programmed. ·        Also, during training data , o    the more machine will work with it o    the more it will get experience and o    the more efficient result is produced . Example:   In Driverless Car , the training data is fed to an Algorithm like   ü   how to Drive a Car on Highway, Busy and Narrow Street with factors like ·        speed limit, ·        parking, ·        stop at signals etc. After that, a Logical and Mathematical model is creat