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Chapter 8

Machine Learning

King Saud UniversityCollege of Computer and Information Sciences Information Technology DepartmentIT422 - Intelligent systems

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Introduction

• What is learning?• Learning in humans consists of (at least):• memorization, comprehension, learning

from examples.• Learning from examples– Square numbers: 1, 4, 9 ,16– 1 = 1 * 1; 4 = 2 * 2; 9 = 3 * 3; 16 = 4 * 4;– What is next in the series?

• We can learn this by example quite easily

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Introduction

• What is learning?“Learning denotes changes in a system that

enable the system to do the same task more efficiently next time”. (Hubert Simon, 1983)

• An agent is learning if it improves its performance on future tasks after making observations about the world.

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Introduction

• What is learning?

• "A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P, if its performance at tasks in T, as measured by P, improves with experience E". (Mitchell, 1997)

• Given: a task T,a performance measure P,some experience E with the task.

• Goal: generalize the experience in a way thatallows to improve the performance on the task.

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Why would we want an agent to learn?

• The designer can not anticipate all situations in which the agent may be. – For example, a robot navigating a maze, robot in space.

• The designer can not anticipate all changes over time. – For example, stock market prediction.

• Sometimes the designers have no idea how to program the solutions themselves (unknown function). – For example: face recognition.

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Components to be learned• Design of a learning element is affected by

– Which component is to be improved– What prior knowledge the agent already has.– What feedback is available to learn from.– What representation is used for the data and the

component

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Components to be learned

Consider an agent training to become a taxi driver

1. When the instructor shouts “Brake!” the agent learn a condition–action rule for when to brake; also when the instructor does not shout .

2. By seeing many camera images that it is told contain buses, it can learn to recognize them.

3. By trying actions and observing the results. Ex. braking hard on a wet road.

4. When it receives no tip from passengers, it can learn a useful component who have been shaken up during the trip of its overall utility function.

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Types of Learning

• In order to learn, the agent needs to observe the world feedback.

• The different types of feedback determine the different types of learning:– Supervised learning– Unsupervised learning– Semi-supervised learning– Reinforcement learning

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Types of Learning• Supervised learning: The agent observes a set of input-output examples (labeled examples) and learns a

map from inputs to outputs.– Classification (Categorization): output is discrete . Learn why certain objects are categorized a

certain way. E.g.: spam email, why are dogs, cats and humans mammals, but trout, mackerel and tuna are fish?– Binary classification (Boolean): there are only two values.– Regression(Prediction): output is real-valued . Learn how to predict how to categorize unseen

objects E.g., Given examples of financial stocks and a categorization of them into safe and unsafe stocks

Learn how to predict whether a new stock will be safe.• Unsupervised learning: No explicit feedback is given, only the inputs (unlabeled examples). The agent

learns patterns in the input. – Ex. “good traffic days”

• Semi-supervised learning: The agent is given some labeled examples (generally a few) and some unlabeled examples and tries to learn a mapping.

• Reinforcement learning: The agent learns from a series of rewards and punishments, and based on these adapts its behavior (e.g. playing chess) .

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Supervised Learning

• Given a training set of N example input-output pairs:(x1, y1), (x2, y2), … (xN, yN),

where, yj = f(xj), where f is unknown function,

the goal is to find a function h that approximates f.• The function h is called a hypothesis.• How to measure the accuracy of h?

– We give a test set of examples, which is different from the training set.

– The hypothesis generalizes well if it correctly predicts the output for the test set.

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How to select a hypothesis

• First, select the hypothesis space: in this case, the set of polynomials.• (a): The line is consistent with the data.• (b): The high-degree polynomial is also consistent with the data.• Ockham’s razor: Choose the simplest hypothesis which is consistent

with the data.

(a) (b)

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Decision Trees

• A decision tree represents a function that has multiple inputs but a single output a “decision”.– We focus on discrete input and Boolean output (Boolean

classification)

• A decision tree reaches the decision by a set of tests on the attributes (the inputs). Thus, the internal nodes are the tests and the leaf nodes are the decisions.

• Example: Test nodes

Decision nodes

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Decision Trees

• A more complex example: deciding to wait at a restaurant:

• The attributes :

1. Alternate: whether there is a suitable alternative restaurant nearby.

2. Bar: whether the restaurant has a comfortable bar area to wait in.

3. Fri I Sat: true on Fridays and Saturdays.

4. Hungry: whether we are hungry.

5. Patrons: how many people are in the restaurant (values are None, Some, and Full).

6. Price: the restaurant's price range ($, $$, $$$).

7. Raining: whether it is raining outside.

8. Reservation: whether we made a reservation.

9. Type: the kind of restaurant (French, Italian, Thai, or burger).

10. WaitEstimate: the wait estimated by the host (0-10 minutes, 10-30, 30-60, or >60).

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Decision Trees

• Classification of examples is positive (T) or negative (F)

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Decision Trees

• This is the real function.• Our goal is to learn this function from examples.

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Decision Trees

• A decision tree can be expressed as propositional logic sentence

(Boolean function) in DNF (disjunctive normal form):

• Goal (Path1 V Path2 V … Pathn), where Pathi= (Attribute1 = Valuek1

Attribute2 = Valuek2 …)

• The same Boolean function can have many representations as a

decision tree (just change the order of the attributes) We want the

smallest possible tree:

• Example: The decision tree of P (Q R)

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Decision TreesQ

F T

RF T

F PF T

F T

RF T

PF T

F T

PF T

F T

PF T

F QF T

RF T

F T

T

A decision tree for the function: P (Q R).

The order of the attributes: P, Q, RThe order of the attributes: Q, R,P

Smaller number of nodes The order is

important

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Decision Trees

• For n (Boolean) attributes there are 2^(2^n) different Boolean functions, and the number of decision trees is much larger (more than n! 2^(2^n) )– Example: n = 6, there are approximately 18.4 x 10^18

possible Boolean functions• Exhaustive search is impossible in practice Learning

the decision tree greedy heuristic search• How to choose the most important attribute and

build the decision tree?– Several algorithms exist. Ex. ID3 (Iterative Dichotomiser 3)

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Summary

• Learning takes many forms, depending on the nature of the agent, the component to be improved, and the available feedback.– Learning can be supervised, unsupervised, semi-

supervised learning, and reinforcement learning, depending on the given feedback.

• Decision trees are powerful tools for classification, they can represent rules in tree structure where each node is either test or decision node.