Steps for Designing Learning System Are

Choose Training Experience Choosing Target FunctionChoosing a Representation of aTarget function Choosing function approximation Final Design.

1. Choosing Training Experience
   The First Design Choice we face is to choose the type of training experience from which our system will learn. The Type of training experience available can have a significant impact on success or failure of the learner. One key Attribute is whether the training experience provides direct or indirect feedback regarding the choices made by the performance system.

A checkers learning problem:

Task – Playing checkers game - T
Performance Measure – percent of games won against opposer -P
Training Experience – playing implementation games against itself -E

In order to complete the design of the learning system we must now choose:

• The Exact type of knowledge to be learned.

• A Representation for this target knowledge.

• A Learning Mechanism.

2. Choosing the Target Function
   The next design choice is to determine exactly what type of knowledge will be learned and how this will be used by the performance program. The program needs only to learn how to choose the best move from among these legal moves. This learning task is representative of a large class of tasks for which the legal moves that define some large search space known a priori, but for which the best search strategy is not known.

For Example
While playing chess with the opponent, when opponent will play then the machine learning algorithm will decide what be the number of possible legal moves taken in order to get success.

3. Choosing a Representation for the Target Function
   When the machine algorithm will know all the possible legal moves the next step is to choose the optimized move using any representation i.e. using linear Equations, Hierarchical Graph Representation, Tabular form etc. The Next Move function will move the Target move like out of these move which will provide more success rate.

For Example
While playing chess machine have 4 possible moves, so the machine will choose that optimized move which will provide success to it.

4. Choosing function approximation
   An optimized move cannot be chosen just with the training data. The training data had to go through with set of example and through these examples the training data will approximates which steps are chosen and after that machine will provide feedback on it.

For Example
When a training data of Playing chess is fed to algorithm so at that time it is not machine algorithm will fail or get success and again from that failure or success it will measure while next move what step should be chosen and what is its success rate.

5. Final Design
   The final design is created at last when system goes from number of examples, failures and success, correct and incorrect decision and what will be the next step etc.

For Example
Deep Blue is an intelligent computer which is ML-based won chess game against the chess expert Garry Kasparov, and it became the first computer which had beaten a human chess expert.