Machine Learning

Introduction

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교재

Machine Learning, Tom T. Mitchell, McGraw-Hill 일부

Reinforcement Learning: An Introduction, R. S. Sutton and A. G. Barto, The MIT Press, 1998 발표

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Machine Learning How to construct computer programs that

automatically improve with experience Data mining(medical applications: 1989), fraudulent credit

card (1989), transactions, information filtering, users’ reading preference, autonomous vehicles, backgammon at level of world champions(1992), speech recognition(1989), optimizing energy cost

Machine learning theory – How does learning performance vary with the number

of training examples presented– What learning algorithms are most appropriate for

various types of learning tasks

기계학습과 통계처리 기계학습과 통계 처리의 차이

– 기계학습 : 데이터에서 일반 규칙을 뽑아 개별 데이터 ( 사건 ) 에 적용

– 통계 : 데이터에서 일어나는 평균적 현상을 개량적으로 평가하여 현상을 설명

기계 학습의 장점– 자질 숫자가 아주 많고 , 데이터 양이 적어도 결과를 얻음– 현상을 모르면서도 결과를 얻을 수 있음– 방법에 따라서는 원인을 설명할 수 있음

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예제 프로그램

http://www.cs.cmu.edu/~tom/mlbook.html– Face recognition– Decision tree learning code– Data for financial loan analysis – Bayes classifier code– Data for analyzing text documents

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이론적 연구

Fundamental relationship among the number of training examples observed, the number of hypotheses under consideration, and the expected error in learned hypotheses

Biological systems

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Def.

A computer program is said to learn from experience E wrt some classes of tasks T and performance P, if its performance at tasks in T, as measured by P, improves with experience E.

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Outline

Why Machine Learning? What is a well-defined learning problem? An example: learning to play checkers What questions should we ask about

Machine Learning?

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Why Machine Learning

Recent progress in algorithms and theory Growing flood of online data Computational power is available Budding industry

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Three niches for machine learning:

Data mining : using historical data to improve decisions– medical records medical knowledge

Software applications we can't program by hand– autonomous driving

– speech recognition

Self customizing programs– Newsreader that learns user interests

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Typical Datamining Task (1/2)

Data :

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Typical Datamining Task (2/2)

Given:– 9714 patient records, each describing a

pregnancy and birth– Each patient record contains 215 features

Learn to predict:– Classes of future patients at high risk for

Emergency Cesarean Section

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Datamining Result

One of 18 learned rules:

If No previous vaginal delivery, and

Abnormal 2nd Trimester Ultrasound, and

Malpresentation at admission

Then Probability of Emergency C-Section is 0.6

Over training data: 26/41 = .63,

Over test data: 12/20 = .60

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Credit Risk Analysis (1/2)

Data :

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Credit Risk Analysis (2/2)

Rules learned from synthesized data:

If Other-Delinquent-Accounts > 2, andNumber-Delinquent-Billing-Cycles > 1

Then Profitable-Customer? = No[Deny Credit Card application]

If Other-Delinquent-Accounts = 0, and(Income > $30k) OR (Years-of-Credit > 3)

Then Profitable-Customer? = Yes[Accept Credit Card application]

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Other Prediction Problems (1/2)

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Other Prediction Problems (2/2)

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Problems Too Difficult to Program by Hand

ALVINN [Pomerleau] drives 70 mph on highways

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Software that Customizes to User

http://www.wisewire.com

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Where Is this Headed? (1/2)

Today: tip of the iceberg– First-generation algorithms: neural nets,

decision trees, regression ...– Applied to well-formatted database– Budding industry

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Where Is this Headed? (2/2)

Opportunity for tomorrow: enormous impact– Learn across full mixed-media data

– Learn across multiple internal databases, plus the web and newsfeeds

– Learn by active experimentation

– Learn decisions rather than predictions

– Cumulative, lifelong learning

– Programming languages with learning embedded?

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Relevant Disciplines

Artificial intelligence Bayesian methods Computational complexity theory Control theory Information theory Philosophy Psychology and neurobiology Statistics . . .

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What is the Learning Problem?

Learning = Improving with experience at some task– Improve over task T,

– with respect to performance measure P,

– based on experience E.

E.g., Learn to play checkers– T: Play checkers

– P: % of games won in world tournament

– E: opportunity to play against self

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Learning to Play Checkers

T: Play checkers P: Percent of games won in world tournament What experience? What exactly should be learned? How shall it be represented? What specific algorithm to learn it?

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Type of Training Experience

Direct or indirect? Teacher or not?

A problem: is training experience

representative of performance goal?

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Choose the Target Function

ChooseMove : Board Move ?? V : Board R ?? . . .

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Possible Definition for Target Function V

if b is a final board state that is won, then V(b) = 100 if b is a final board state that is lost, then V(b) = -100 if b is a final board state that is drawn, then V(b) = 0 if b is not a final state in the game, then V(b) = V(b'),

where b' is the best final board state that can be achieved

starting from b and playing optimally until the end of the game.

This gives correct values, but is not operational

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Choose Representation for Target Function

collection of rules? neural network ? polynomial function of board features? . . .

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A Representation for Learned Function

w0+ w1·bp(b)+w2·rp(b)+w3·bk(b)+w4·rk(b)+w5·bt(b)+w6·rt(b)

bp(b) : number of black pieces on board b rp(b) : number of red pieces on b bk(b) : number of black kings on b rk(b) : number of red kings on b bt(b) : number of red pieces threatened by black

(i.e., which can be taken on black's next turn) rt(b) : number of black pieces threatened by red

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Obtaining Training Examples

V(b): the true target function V(b) : the learned function Vtrain(b): the training value

One rule for estimating training values: Vtrain(b) V(Successor(b))

^

^

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Choose Weight Tuning Rule

LMS Weight update rule:Do repeatedly: Select a training example b at random

1. Compute error(b):

error(b) = Vtrain(b) – V(b)

2. For each board feature fi, update weight wi:

wi wi + c · fi · error(b)

c is some small constant, say 0.1, to moderate the rate of learning

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Final design

The performance system– Playing games

The critic– 차이 발견 ( 분석 )

The generalizer – Generate new hypothesis

The experiment generator– Generate new problems

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학습방법

Backgammon : 6 개 feature 를 늘여서– Reinforcement learning – Neural network ::: 판 자체 , 100 만번 스스로 학습

인간에 필적할 만함– Nearest Neighbor algorithm : 여러 가지 학습자료를

저장한 후 가까운 것을 찾아서 처리 – Genetic algorithm ::: 여러 프로그램을 만들어

적자생존을 통해 진화– Explanation-based learning ::: 이기고 지는 이유에

대한 분석을 통한 학습

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Design Choices

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Some Issues in Machine Learning

What algorithms can approximate functions well (and when)?

How does number of training examples influence accuracy?

How does complexity of hypothesis representation impact it?

How does noisy data influence accuracy? What are the theoretical limits of learnability? How can prior knowledge of learner help? What clues can we get from biological learning systems? How can systems alter their own representations?