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King Saud University College of Computer and Information Sciences Information Technology Department IT422 - Intelligent systems. Chapter 8. Machine Learning. Introduction. What is learning? Learning in humans consists of (at least): memorization, comprehension, learning from examples.

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chapter 8

King Saud University

College of Computer and Information Sciences

Information Technology Department

IT422 - Intelligent systems

Chapter 8

Machine Learning

introduction
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
introduction1
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.
introduction2
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 that allows to improve the performance on the task.
why would we want an agent to learn
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.
components to be learned
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
components to be learned1
Components to be learned

Consider an agent training to become a taxi driver

  • When the instructor shouts “Brake!” the agent learn a condition–action rule for when to brake; also when the instructor does not shout .
  • By seeing many camera images that it is told contain buses, it can learn to recognize them.
  • By trying actions and observing the results. Ex. braking hard on a wet road.
  • 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.
types of learning
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
types of learning1
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) .
supervised learning
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.
how to select a hypothesis
How to select a hypothesis

(a)

(b)

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.

decision trees
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:

Decision nodes

Test nodes

decision trees1
Decision Trees
  • A more complex example: deciding to wait at a restaurant:
  • The attributes :
    • Alternate: whether there is a suitable alternative restaurant nearby.
    • Bar: whether the restaurant has a comfortable bar area to wait in.
    • Fri I Sat: true on Fridays and Saturdays.
    • Hungry: whether we are hungry.
    • Patrons: how many people are in the restaurant (values are None, Some, and Full).
    • Price: the restaurant's price range ($, $$, $$$).
    • Raining: whether it is raining outside.
    • Reservation: whether we made a reservation.
    • Type: the kind of restaurant (French, Italian, Thai, or burger).
    • WaitEstimate: the wait estimated by the host (0-10 minutes, 10-30, 30-60, or >60).
decision trees2
Decision Trees
  • Classification of examples is positive (T) or negative (F)
decision trees3
Decision Trees
  • This is the real function.
  • Our goal is to learn this function from examples.
decision trees4
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)
decision trees5
Decision Trees

The order of the attributes: Q, R,P

The order of the attributes: P, Q, R

Smaller number of nodes  The order is important

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

decision trees6
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)
summary
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.