MODULE $1-$ INTRODUCTION AND CONCEPT LEARNING

Define Machine Learning. Explain with examples why machine learning is important.

Discuss some applications of machine learning with examples.

Explain how some disciplines have influenced machine learning.

Describe the following problems with respect to Tasks, Performance, and Experience:

A Checkers learning problem

A Handwritten recognition learning problem

A Robot driving learning problem

Explain the steps in designing learning systems in detail.

Explain different perspectives and issues in machine learning.

Write the FIND$-$S algorithm and explain with the example given below

$\backslash$table$[[$Example$,$Sky$,$AirTemp,Humidity,Wind,Water,Forecast,EnjoySport$],[1,$Sunny,Warm,Normal,Strong,Warm,Same,Yes$],[2,$Sunny,Warm,High,Strong,Warm,Same,Yes$],[3,$Rainy,Cold,High,Strong,Warm,Change,No$],[4,$Sunny,Warm,High,Strong,Cool,Change,Yes$]]$

A computer program is said to learn from experience $E$ with respect to some task $T$ and some performance measure $P$ if its performance on $T,$ as measured by $P,$ improves with experience E$.$

Suppose we feed a learning algorithm a lot of historical weather data, and have it learn to predict weather. What would be a reasonable choice for $P?$

a$)$ The probability of it correctly predicting a future date's weather.

b$)$ None of these.

c$)$ The weather prediction task.

d$)$ The process of the algorithm examining a large amount of historical weather data.