Introduction to Machine Learning

  UNIT 1 Introduction to Machine Learning

1)      An Introduction to Machine Learning:

Machine learning is an application of AI that provides systems the ability to automatically learn from the experience without being explicitly programmed.

Definition of ML: “A computer program is said to learn from experience E with respect to some task T and performance measure P, if its performance at tasks in T improves with experiences E”.

Examples:  Checkers Learning

T – play checkers

P – percentage of games won in the world tournament.

E - train with peers.

·       Handwriting recognition

T – classifying handwritten words within images.

P – percent of words correctly classified.

E – database of handwritten words with given classifications.

Other Examples includes: Robot Driving, Spam Filtering Problem, Fruit Prediction Problem, and Face Recognition Problem etc.

Well Posed Learning Problems:  Any problem can be classified as well posed learning problem if it has three features: 1. Task                              2. Performance Measure                           3. Experience

Note - Machine Learning System has 3 important features –  Remember - system must remember what it is learning  • 2+3 = 5 {Seen Data} • 10+2 = 12 {Seen Data} • 2+2 = 4 {Unseen Data}  Adapt - become adjusted to new conditions.  Generalize - The ability to identify the rules, to generalize, allows the system to make predictions on unknown data.

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