# Design a Learning System in Machine Learning

Included

- Choosing Training

Experience - Choosing Training

Function - Choosing the

Representation for Target Function - Choosing Function

Approximation Algorithm - Final Design

**Step 1: Choosing Training Experience**

Choosing Training

Experience plays an important role in success and failure learning of learner

or machine.

First factor for choosing

Training Experience is **how to provide experience to learner**. We can able

to provide experience to the learn in a form of direct Feed Back or indirect

feedback. In the Direct Feed Back learner directly Learn from examples like

different chess board state and moves. In the case Indirect feedback, learner

learn by experiencing it. In this learner provided with move sequences and

final outcomes of various games played further learner play the game and assign

credit or blame to every move in the sequence like credit of win and blame of

loss. every sequence of move. Assigning credit for wining and loss to every

move is again a challenging task because output further depend on sequence of

each and every move like initial highly credited move can leads to failure due

to further move. So, we can conclude that direct experience is much easier than

indirect feedback.

**Second important factor
for choosing Training experience is the degree to which the learner controls
the sequence of training examples** i.e., full dependent,

partially dependent and completely independent

In the case of fully

dependent learner is fully dependent on teacher in selecting the board state

and corresponding action.

Partially dependent case

is that, where learner partially dependent on teacher in selecting the board

state and corresponding action, he will ask to the teacher only when there is

any confusion.

Completely independent

mean learner is not dependent on teacher, he explores all the boards state and

their corresponding move by their own.

Third important attribute

is how much training experience satisfies the performance measure P on which

final performance of system is going to be measure.

For example, suppose performance

measure P for learner learning Cricket is number of match win by the learner on

the world tournament.

The training experience

provided to the learner to playing cricket against itself is not satisfy the

performance measure P mean we should provide an experience to the learner in

which learner play cricket against itself, at school level, interschool level,

inter state level. We can conclude that there is need to choose the training

experience that, should cover all the aspect needed to satisfy performance

measure.

**Step2: Choosing Training Function **

Target function basically

talk about what type of knowledge is going to be learned and how it will be

used to measure the performance.

Letâ€™s consider an example

In Chess game the learner has to learn about how choose best move out of all

possible legal moves for any particular board situation.

Let **Select Move** be

the target function and the notation are

**Select Move: BSâ†’ LM**

where **Select Move **function

accepts any board from the set of legal board states BS as input and generate

some move from the set of legal moves LM as output.

**Select Move**

is a choice for the target function in chess game example, but this function

seems to be very difficult to learn indirect training experience. So, there is

need of some modification.

An alternative target function

is Evaluation function which assign real value to legal moves according to

board state.

Let the target

function T and the notation

T:

BS â†’ RV

which denote that T assign some real value to legal board state from

the set BS. target function T is Intended to assign higher scores to better

board states. If the system learns such a target function T, then it can use it

to select the best move from any current board position.

in BS Set, as follows:

- If bs is a final state of chess board that is won, then T(bs) = 100
- If bs is a final state of chess board that is lost, then T(bs) = -100
- If bs is a final state of chess board that is drawn, then T(bs) = 0
- If bs is a not a final state of chess board in the game, then T(bs)

= T(bs’ ),

Where bs’ is the best final board state that can be achieved starting

from bS and playing optimally until the end of the game.

**Step 3 Choosing the Representation for Target Function
**

As we know target

function talk about what to learn, there is need of representing target

function. Representation is used by learning algorithm to describe the function

T.

**Step 4 Choosing Function Approximation Algorithm**

**Step 5 Final Design**