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Lecture 1: Basic Concepts ofMachine Leaning

Cognitive Systems II - Machine LearningSS 2005

Ute Schmid (lecture)

Emanuel Kitzelmann (practice)

Maximilian Roeglinger (tutor)

Applied Computer Science, Bamberg University

Lecture 1: Basic Concepts of Machine Leaning – p. 1

Organization of the Course

Homepage:http://www.cogsys.wiai.uni-bamberg.de/teaching/

Prerequesites: CogSys I

Textbook: Tom Mitchell (1997). Machine Learning.McGraw Hill.

PracticeProgramming Assignments in JavaMarked exercise sheets and extra points for the exam


http://www.cogsys.wiai.uni-bamberg.de/teaching/

Outline of the Course

Basic Concepts of Machine Learning

Basic Approaches to Concept LearningFoundations of Concept LearningDecision TreesPerceptrons and Multilayer-PerceptronsHuman Concept Learning

Special Aspects of Concept LearningInductive Logic ProgrammingGenetic AlgorithmsInstance-based LearningBayesian Learning


Outline of the Course

Learning Programs and StrategiesReinforcement LearningInductive Function Synthesis

Further Topics and Applications in Machine Learning


Course Objectives

introduce to the central approaches of machinelearning

point out relations to human learning

define a class of problems that encompassesinteresting forms of learning

explore algorithms that solve such problems

provide understanding of the fundamental structure oflearning problems and processes


Some Quotes as Motivation

If an expert system–brilliantly designed, engineered andimplemented–cannot learn not to repeat its mistakes, it isnot as intelligent as a worm or a sea anemone or a kitten.

Oliver G. Selfridge, from The Gardens of Learning

Find a bug in a program, and fix it, and the program willwork today. Show the program how to find and fix a bug,and the program will work forever.

Oliver G. Selfridge, in AI’s Greatest Trends and Controversies

If we are ever to make claims of creating an artificialintelligence, we must address issues in natural language,automated reasoning, and machine learning.

George F. Luger


What is Machine Learning?

Some definitions

Machine learning refers to a system capable of theautonomous acquisitionand integration of knowledge. Thiscapacity to learn from experience, analytical observation,and other means, results in a system that cancontinuously self-improveand thereby offer increasedefficiency and effectiveness.

http://www.aaai.org/AITopics/html/machine.html

The field of machine learning in concerned with thequestion of how to construct computer programmsthatautomatically improvewith experience.

Tom M. Mitchell, Machine Learning (1997)


http://www.aaai.org/AITopics/html/machine.html

What is Machine Learning?

machine learning is inherently a multidisciplinary fieldi.e. artificial intelligence, probability, statistics,computational complexity theory, informationtheory, philosophy, psychology, neurobiology, ...

Knowledge-based vs. Learning Systems

Different approaches

Concept vs. Classification LearningSymbolic vs. Statistical LearningInductive vs. Analytical Learning



Knowledge-based Systems:Acquisition and modeling ofcommon-sense knowledge and expert knowledge⇒ limited to given knowledge base and rule set⇒ Inference: Deduction generates no new knowledge

but makes implicitly given knowledge explicit⇒ Top-Down: from rules to facts

Learning Systems: Extraction of knowledge and rules fromexamples/experience

Teach the system vs. program the systemLearning as inductive process

⇒ Bottom-Up: from facts to rules



⇒ A flexible and adaptive organism cannot rely on a fixedset of behavior rules but must learn (over its completelife-span)!

⇒ Motivation for Learning Systems


Knowledge Acquisition Bottleneck

En

gin

eerin

g

Acq

uis

itio

n Knowledge

EXPERTSYSTEM

(Feigenbaum, 1983)

Break-through in computerchess with Deep Blue:Evaluation function of chessgrandmaster Joel Benjamin.Deep Blue cannot change theevaluation function by itself!

Experts are often not able toverbalize their specialknowledge.⇒ Indirect methods: Extractionof knowledge from expertbehavior in example situations(diagnosis of X-rays, controllinga chemical plant, ...)


Learning as Induction

Deduction Induction

All humans are mortal. (Axiom) Socrates is human. (Background K.)

Socrates is human. (Fact) Socrates is mortal. (Observation(s))

Conclusion: Generalization:

Socrates is mortal. All humans are mortal.

Deduction: from general to specific ⇒ proven correctness

Induction: from specific to general ⇒ (unproven)knowledge gain

Induction generates hypotheses notknowledge!


Epistemological problems

⇒ pragmatic solutions

Confirmation Theory: A hypothesis obtained bygeneralization gets supported by new observations(not proven!).

grue Paradox:All emeralds are grue.Something is grue, if it is green before a future time tand blue thereafter.⇒ Not learnable from examples!


Inductive Learning Hypothesis

as shown above inductive learning is not provencorrect

nevertheless the learning task is determine ahypothesis h ∈ H identical to the target concept c

(∀x ∈ X)[h(x) = c(x)]

only training examples D are available

inductive algorithms can at best guarantee that theoutput hypothesis D fits the target concept over D

(∀x ∈ D)[h(x) = c(x)]

⇒ Inductive Learning Hypothesis: Any hypothesis found toapproximate the target concept well over a sufficientlylarge set of training examples will also approximate thetarget function well over other unobserved examples


Concept vs. Classification Learning

Concept learning:

Objects are clustered in concepts.

Extensional: (infinite) set of all exemplarsIntensional: finite characterizationT = {x| has-3/4-legs(x), has-top(x)} �

��

��

��

��

Construction of a finite characterization from a subsetof examples (“training set”).

Classification learning:

Identification of relevant attributes and theirinterrelation, which characterize an object as memberof a class.f : Xn → K


Concept Learning / Examples

+ Occurence of Tse-Tse fly yes/no, given geographicand climatic attributes+ risk of cardiac arrest yes/no, given medical data+ credit-worthinessof customer yes/no, given personaland customer data+ safe chemical process yes/no, given physical andchemical measurements

Generalization of pre-classified example data,application for prognosis


Symbolic vs. Statistical Learning

Symbolic Learning: underlying learning strategies such asrote learning, learning by being told, learning byanalogy, learning from examples and learning fromdiscovery

knowledge is represented in the form of symbolicdescriptions of the learned concepts, i.e.production rules or concept hierarchiesi.e. decision trees, inductive logic programming

Statistical Learning: modeling of the behavioursometimes called statistical inference,connectionstic learningi.e. Artificial Neuronal Networks, BayesianLearning


Designing a Learning System

learning system: A computer program is said to learnfrom experience E with respect to some class of tasksT and performance measure P , if its performance attasks in T , as measured by P , improves withexperience E.

i.e. a checkers learning problemT : playing checkersP : percent of games won against opponentsE: playing practice games against itself



consider designing a program to learn to play checkersin order to illustrate some of the basic design issuesand approaches to machine learning

1. Choosing the Training Experiencedirect or indirect feedbackdegree to which the learner controls the sequenceof training examplesrepresentativity of the distribution of the trainingexamples

⇒ significant impact on success or failure



2. Choosing the Target Function

determine what type of knowledge will be learned

most obvious form is a function, that chooses the best move forany given board state

i.e., ChooseMove : B → M

sometimes evaluation functions are easier to learn

i.e., V : B → <

assigns higher values to better boards states by recursivelygenerating the successor states and evaluating them until thegame is over

no efficient solution (nonoperational definition)

⇒ function approximation is sufficient (operational definition)



3. Choosing a Representation for the Target Function

e.g. a large table, a set of rules or a linear function

e.g. V (b) = w0 + w1x1 + w2x2 + ... + wixi

4. Choosing a Function Approximation Algorithm

5. Estimating Training Values

it is easy to assign a value to a final state

the evaluation of intermediate states is more difficzult



DetermineTarget Function

Determine Representationof Learned Function

Determine Type of Training Experience

DetermineLearning Algorithm

Games against self

Games against experts Table of correct

moves

Linear functionof six features

Artificial neural network

Polynomial

Gradient descent

Board ➝ value

Board➝ move

Completed Design

...

...

Linear programming

...

...


Notation

Instance SpaceX: set of all possible examples over which theconcept is defined (possibly attribute vectors)

Target Conceptc : x → {0, 1}: concept or function to be learned

Training example x ∈ X of the form < x, c(x) >

Training Set D: set of all available training examples

Hypothesis SpaceH: set of all possible hypotheses according to thehypothesis language

Hypothesish ∈ H: boolean valued function of the form X → {0, 1}

⇒ the goal is to find a h ∈ H, such that (∀x ∈ X)[h(x) = c(x)]


Properties of Hypotheses

general-to-specific ordering

naturally occuring order over H

learning algorithms can be designed to search H exhaustivelywithout explicitly enumerating each hypothesis h

hi is more general or equal to hk (written hi ≥g hk)⇔ (∀x ∈ X)[(hk(x) = 1) → (hj(x) = 1)]

hi is (stricly) more general to hk (written hi >G hk)⇔ (hj ≥g hk) ∧ (hk �g hi)

≥g defines a partial ordering over the Hypothesis Space H


Properties of Hypotheses - Example

h1 = Aldo loves playing Tennis if the sky is sunnyh2 = Aldo loves playing Tennis if the water is warmh3 = Aldo loves playing Tennis if the sky is sunny and the water is warm

⇒ h1 >g h3, h2 >g h3, h2 6> h1


Properties of Hypotheses

consistency

a hypothesis h is consistentwith a set of training examples D iffh(x) = c(x) for each example < x, c(x) > in D

Consistent(h, D) ≡ (∀ < x, c(x) >∈ D)[h(x) = c(x)]

that is, every example in D is classified correctly by thehypothesis


Properties of Hypotheses - Example

h1 is consistent with D


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Lecture 1: Basic Concepts of Machine Leaning - · PDF fileBasic Approaches to Concept Learning ... Basic Concepts of Machine Leaning ... learning algorithms can be designed to search