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Data Mining & Machine LearningIntroduction

Intelligent Systems Lab.

Soongsil University

Thanks to Raymond J. Mooney in the University of Texas at Austin, Isabelle Guyon

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Artificial Intelligence (AI): Research Areas

ArtificialIntelligence

Research

Rationalism (Logical)Empiricism (Statistical)Connectionism (Neural)Evolutionary (Genetic)Biological (Molecular)

Paradigm

Application

Intelligent AgentsInformation RetrievalElectronic CommerceData MiningBioinformaticsNatural Language Proc.Expert Systems

Learning AlgorithmsInference MechanismsKnowledge RepresentationIntelligent System Architecture

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Artificial Intelligence (AI): Paradigms

Symbolic AI Rule-Based Systems

Connectionist AI Neural Networks

Evolutionary AI Genetic Algorithms

Molecular AI: DNA Computing

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What is Machine Learning?

Learning

algorithm

TRAININGDATA Answer

Trained

machine

Query

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Definition of learning

ProgramLearning Program

LearnedProgram

Experience, E Task, T

Performance, P

Task

Performance

• Definition: 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

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What is Learning?

• Herbert Simon: “Learning is any process by which a system improves performance from experience.”

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Machine Learning

• Supervised Learning– Estimate an unknown mapping from known input- output pairs

– Learn fw from training set D={(x,y)} s.t.

– Classification: y is discrete– Regression: y is continuous

• Unsupervised Learning– Only input values are provided

– Learn fw from D={(x)} s.t.

– Clustering

)()( xxw fyf

xxw )(f

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Why Machine Learning?

• Recent progress in algorithms and theory• Growing flood of online data• Computational power is available• Knowledge engineering bottleneck. Develop systems

that are too difficult/expensive to construct manually because they require specific detailed skills or knowledge tuned to a specific task

• Budding industry

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Niches using machine learning

• Data mining from large databases.– Market basket analysis (e.g. diapers and beer)– Medical records → medical knowledge

• Software applications we can’t program by hand– Autonomous driving– Speech recognition

• Self customizing programs to individual users.– Spam mail filter– Personalized tutoring– Newsreader that learns user interests

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Trends leading to Data Flood

• More data is generated:– Bank, telecom, other

business transactions ...

– Scientific data: astronomy, biology, etc

– Web, text, and e-commerce

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Big Data Examples

• Europe's Very Long Baseline Interferometry (VLBI) has 16 telescopes, each of which produces 1 Gigabit/second of astronomical data over a 25-day observation session – storage and analysis a big problem

• AT&T handles billions of calls per day– so much data, it cannot be all stored -- analysis has to

be done “on the fly”, on streaming data

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Largest databases in 2007

• Commercial databases:– AT&T: 312 TB– World Data Centre for Climate: 220 TB– YouTube: 45TB of videos – Amazon: 42 TB (250,000 full textbooks)– Central Intelligence Agency (CIA): ?

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Data Growth

In 2 years, the size of the largest database TRIPLED!

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Machine Learning / Data Mining Application areas

• Science– astronomy, bioinformatics, drug discovery, …

• Business– CRM (Customer Relationship management), fraud

detection, e-commerce, manufacturing, sports/entertainment, telecom, targeted marketing, health care, …

• Web: – search engines, advertising, web and text mining, …

• Government– surveillance, crime detection, profiling tax cheaters, …

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Data Mining for Customer Modeling

• Customer Tasks:– attrition prediction– targeted marketing:

• cross-sell, customer acquisition

– credit-risk– fraud detection

• Industries– banking, telecom, retail sales, …

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Customer Attrition: Case Study

• Situation: Attrition rate at for mobile phone customers is around 25-30% a year !

• With this in mind, what is our task?– Assume we have customer information for the

past N months.

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Customer Attrition: Case Study

Task:

• Predict who is likely to attrite next month.

• Estimate customer value and what is the cost-effective offer to be made to this customer.

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Customer Attrition Results

• Verizon Wireless built a customer data warehouse

• Identified potential attriters

• Developed multiple, regional models

• Targeted customers with high propensity to accept the offer

• Reduced attrition rate from over 2%/month to under 1.5%/month (huge impact, with >30 M subscribers)

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Assessing Credit Risk: Case Study

• Situation: Person applies for a loan

• Task: Should a bank approve the loan?

• Note: People who have the best credit don’t need the loans, and people with worst credit are not likely to repay. Bank’s best customers are in the middle

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Credit Risk - Results

• Banks develop credit models using variety of machine learning methods.

• Mortgage and credit card proliferation are the

results of being able to successfully predict if a person is likely to default on a loan

• Widely deployed in many countries

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Successful e-commerce – Case Study

• Task: Recommend other books (products) this person is likely to buy

• Amazon does clustering based on books bought:– customers who bought “Advances in Knowledge

Discovery and Data Mining”, also bought “Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations”

• Recommendation program is quite successful

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Security and Fraud Detection - Case Study

• Credit Card Fraud Detection• Detection of Money laundering

– FAIS (US Treasury)

• Securities Fraud– NASDAQ KDD system

• Phone fraud– AT&T, Bell Atlantic, British Telecom/MCI

• Bio-terrorism detection at Salt Lake Olympics 2002

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Example Problem:Handwritten Digit Recognition

• Handcrafted rules will result in large no. of rules and Exceptions

• Better to have a machine that learns from a large training set

Wide variability of same numeral

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Chess Game

– Let IBM’s stock increase

by $18 billion at that year

In 1997, Deep Blue(IBM) beat Garry Kasparov( 러 ).

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Some Successful Applications ofMachine Learning

• Learning to drive an autonomous vehicle

– Train computer-controlled vehicles

to steer correctly

– Drive at 70 mph for 90 miles on public

highways

– Associate steering commands with

image sequence

– 1200 computer-generated images as

training examples

• Half-hour training An additional information from previous image indicating the darkness or lightness of the road

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Some Successful Applications ofMachine Learning

• Learning to recognize spoken words– Speech recognition/synthesis

– Natural language understanding/generation

– Machine translation

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Example 1: visual object categorization

• A classification problem: predict category y based on image x.• Little chance to “hand-craft” a solution, without learning.• Applications: robotics, HCI, web search (a real image Google..)

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Face Recognition - 1

Given multiple angles/ views of a person, learn to identify them.

Learn to distinguishmale from female faces.

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Face Recognition - 2

Learn to recongnize emotions, gestures

Li, Ye, Kambhametta, 2003

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Robot

• Sony AIBO robot – Available on June 1, 1999 – Weight: 1.6 KG – Adaptive learning and growth capabilities – Simulate emotion such as happiness and anger

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Robot

• Honda ASIMO (Advanced Step in Innovate MObility)

– Born on 31 October, 2001

– Height: 120 CM, Weight: 52 KG

http://blog.makezine.com/archive/2009/08/asimo_avoids_moving_obstacles.html?CMP=OTC-0D6B48984890

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Biomedical / Biometrics

• Medicine:– Screening– Diagnosis and prognosis– Drug discovery

• Security:– Face recognition– Signature / fingerprint – DNA fingerprinting

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Computer / Internet

• Computer interfaces:– Troubleshooting wizards – Handwriting and speech– Brain waves

• Internet– Spam filtering– Text categorization– Text translation– Recommendation

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Classification

• Assign object/event to one of a given finite set of categories.– Medical diagnosis– Credit card applications or transactions– Fraud detection in e-commerce– Worm detection in network packets– Spam filtering in email– Recommended articles in a newspaper– Recommended books, movies, music, or jokes– Financial investments– DNA sequences– Spoken words– Handwritten letters– Astronomical images

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Problem Solving / Planning / Control

• Performing actions in an environment in order to achieve a goal.– Solving calculus problems– Playing checkers, chess, or backgammon– Driving a car or a jeep– Flying a plane, helicopter, or rocket– Controlling an elevator– Controlling a character in a video game– Controlling a mobile robot

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Applications

inputs

training examples

10

102

103

104

105

Bioinformatics

Ecology

OCRHWR

MarketAnalysis

TextCategorization

Machine Vision

Syst

em d

iagn

osis

10 102 103 104 105

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Disciplines Related with Machine Learning

• Artificial intelligence– 기호 표현 학습 , 탐색문제 , 문제해결 , 기존지식의

활용

• Bayesian methods– 가설 확률계산의 기초 , naïve Bayes classifier,

unobserved 변수 값 추정

• Computational complexity theory– 계산 효율 , 학습 데이터의 크기 , 오류의 수 등의

측정에 필요한 이론적 기반

• Control theory– 이미 정의된 목적을 최적화하는 제어과정과 다음 상태

예측을 학습

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• Information theory– Entropy 와 Information Content 를 측정 , Minimum

Description Length, Optimal Code 와 Optimal Training 의 관계

• Philosophy– Occam’s Razor, 일반화의 타당성 분석

• Psychology and neurobiology– Neural network models

• Statistics– 가설의 정확도 추정시 발생하는 에러의 특성화 , 신뢰구간 ,

통계적 검증

Disciplines Related with Machine Learning (2)

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Definition of learning

ProgramLearning Program

LearnedProgram

Experience, E Task, T

Performance, P

Task

Performance

• Definition: 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

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Example: checkers

Task T: Playing checkers.

Performance measure P: % of games won.

Training experience E: Practice games by playing against itself.

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Example: Recognizing handwritten letters

Task T: Recognizing and classifying handwritten words within images.

Performance measure P: % words correctly classified.

Training experience E: A database of handwritten words with given classifications.

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Example: Robot driving

Task T: Driving on public four-lane highway using vision sensors.

Performance measure P: Average distance traveled before an error (as judged by a human overseer).

Training experience E: A sequence of images and steering commands recorded while observing a human driver.

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Designing a learning system

Task T: Playing checkers.Performance measure P: % of games won.Training experience E: Practice games by playing against itself.

– What does this mean?– and what can we learn from it?

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Measuring Performance

• Classification Accuracy• Solution correctness• Solution quality (length, efficiency)• Speed of performance

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Designing a Learning System

1. Choose the training experience2. Choose exactly what is to be learned, i.e. the target

function.3. Choose how to represent the target function.4. Choose a learning algorithm to infer the target function

from the experience.

Environment/Experience

Learner

Knowledge

PerformanceElement

Train examples…

Test examples…

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Designing a Learning System

1. Choosing the Training Experience

• Key Attributes– Direct/indirect feedback 을 제공하는가 ?

• Direct feedback: checkers states and correct move• Indirect feedback: move sequences and final outcomes of various games

– Credit assignment problem

– Degree of controlling the sequence of training example• Learner 의 자율성 , 학습 정보를 얻을 때 teacher 의

도움을 받는 정도 ,

– Distribution of examples• Train examples 의 분포와 Test examples 의 분포의 문제• 시스템의 성능을 평가하는 테스트의 예제 분포를 잘

반영해야 함• 특수한 경우의 분포가 반영 (The Checkers World Champion

이 격은 특수한 경우의 분포가 반영 될까 ?)

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Training Experience

• Direct experience: Given sample input and output pairs for a useful target function.– Checker boards labeled with the correct move,

• e.g. extracted from record of expert play

• Indirect experience: Given feedback which is not direct I/O pairs for a useful target function.– Potentially arbitrary sequences of game moves and their

final game results.– Credit/Blame Assignment Problem: How to assign credit/

blame to individual moves given only indirect feedback?

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Source of Training Data

• Provided random examples outside of the learner’s control. ( 학습자 - 알고리즘 판단 밖의 사례를 무작위 추출 )– Negative examples available or only positive?

• Good training examples selected by a “benevolent teacher.” (Teacher 로 부터 )

• Learner can construct an arbitrary example and query an oracle for its label. ( 시스템 자체에서 예제를 구축 )

– Learner can design and run experiments directly in the environment without any human guidance.

( 사람의 도움없이 시스템이 새로운 문제틀을 제시함 )

– Learner can query an oracle about class of an unlabeled example in the environment. ( 불안전한 예제의 답을 질문 , 즉 (x, ___) 입력은 있으나 결과값이 없는 경우 )

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Training vs. Test Distribution

• Generally assume that the training and test examples are independently drawn from the same overall distribution of data.– IID: Independently and identically distributed

• If examples are not independent, requires collective classification.

(e.g. communication network, financial transaction network, social network 에 대한 관계 모델구축 시 )

• If test distribution is different, requires transfer learning. that is, achieving cumulative learning

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Transfer learning

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참고 : Transfer learning

• Transfer learning is what happens when someone finds it much easier to learn to play chess having already learned to play checkers;

or to recognize tables having already learned to recognize chairs;

or to learn Spanish having already learned Italian.

• Achieving significant levels of transfer learning across tasks -- that is, achieving cumulative learning -- is perhaps the central problem facing machine learning.

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Designing a Learning System

1. Choose the training experience

2. Choose exactly what is to be learned, i.e. the target function.

3. Choose how to represent the target function.

4. Choose a learning algorithm to infer the target function from the experience.

Environment/Experience

Learner

Knowledge

PerformanceElement

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• 어떤 지식 ( 함수 ) 을 학습시키고 , 평가 시스템에 의하여 어떻게 이용 되어 질 것인가 ?

• 가정 : “ 장기게임에서 , 현재의 장기판에서 타당한 움직임들을 생성하는 함수가 있고 , 최고의 움직임을 선택한다” .

– Could learn a function: 1. ChooseMove : B →M( 최고의 움직임 )

Or

2. Evaluation function, V : B → R * 각 보드의 위치에 따라 얼마나 유리한가에 대한 점수를 부여함 . * V 는 각각의 움직임에 대한 결과의 점수에 따라 선택하는데 이용가능 하여 * 결국은 최고 높은 점수를 얻을 수 있는 움직임을 선택하게 한다 .

2. Choosing a Target Function

Designing a Learning System

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• A function that chooses the best move M for any B– ChooseMove : B →M– Difficult to learn

• It is useful to reduce the problem of improving performance P at task T, to the problem of learning some particular target function.

• An evaluation function that assigns a numerical score to any B– V : B → R

Designing a Learning System

2. Choosing the Target Function

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The start of the learning work

Instead of learning ChooseMove we establish a value function:

target function, V : B → R

that maps any legal board state in B to some real value in R.

어떤 Position 에서도 그 Position 이 초래하게 되는 Score 를 최대화 시키는 움직임을 선택하도록 한다 .

1. if b is a final board state that is won, then V (b) = 100.2. if b is a final board state that is lost, then V (b) = −100.3. if b is a final board state that is drawn, then V (b) = 0.4. if b is not a final board state, then V (b) =

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The start of the learning work

Instead of learning ChooseMove we establish a value function:

target function, V : B → R

that maps any legal board state in B to some real value in R..어떤 Position 에서도 그 Position 이 초래하게 되는 Score 를 최대화 시키는

움직임을 선택하도록 한다 .

1. if b is a final board state that is won, then V (b) = 100.2. if b is a final board state that is lost, then V (b) = −100.3. if b is a final board state that is drawn, then V (b) = 0.4. if b is not a final board state, then V (b) = V (b’), b’ : 성취될 수 있는 최고의 마지막 상태 (the best final board state) ( 단 상대방도 최적으로 수를 둔다고 가정 )

Unfortunately, this did not take us any further!

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Approximating V(b)

• Computing V(b) is intractable since it involves searching the complete exponential game tree.

• Therefore, this definition is said to be non-operational.

• An operational definition can be computed in reasonable (polynomial) time.

• Need to learn an operational approximation to the ideal evaluation function.

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Designing a Learning System

1. Choose the training experience

2. Choose exactly what is to be learned, i.e. the target function.

3. Choose how to represent the target function.

4. Choose a learning algorithm to infer the target function from the experience.

Environment/Experience

Learner

Knowledge

PerformanceElement

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3. Choosing a Representation for the Target Function

• Describing the function – Tables

– Rules

– Polynomial functions

– Neural nets

• Trade-off in choice– Expressive power( 함수의 표현 )

– Size of training data ( 예제의 크기 )

• 표현 ( 제약조건 ) 이 많을 수록 함수의 해의 결과는 더 좋아 진다 . 즉 더욱 좋은 근사값을 얻는 함수를 구할 수 있다 .

• 그러나 정확한 함수를 얻기 위해서 더 많은 예제 , 사이즈가 큰 예제가 필요하게 된다 .

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Approximate representation

w1 - w6: weights

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Linear Function for Representing V(b)

• Use a linear approximation of the evaluation function.

(b) = w0 + w1x1 + w2x2 + w3x3 +w4x4 + w5x5 + w6x6

w0 : an additive constant

^

V

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Designing a Learning System

1. Choose the training experience

2. Choose exactly what is to be learned, i.e. the target function.

3. Choose how to represent the target function.

4. Choose a learning algorithm to infer the target function from the experience.

Environment/Experience

Learner

Knowledge

PerformanceElement

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4. Choosing a Function Approximation Algorithm

A training example is represented as an ordered pair <b, Vtrain(b) >

b: board stateVtrain(b) : training value for b

Instance: “black has won the game <<x1=3, x2=0, x3=1, x4=0, x5=0, x6=0>, +100> (x2 = 0) indicates that white has no remaining pieces.

Estimating training values for intermediate board states

Vtrain(bi) ← ( bi+1 ) = (Successor(bi))

: current approximation to V, ( 즉 the learned function, hypothesis)

Successor(bi): the next board state, 즉 bi+1 state

* 현재의 b 보드상태에 대한 training value 은 ← 다음단계의 장기판 상태 (b+1) 의 가설의 함수값을 사용

^

V

^

V^

V

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Estimating Training Values

DESIGNING A LEARNING SYSTEM

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부연설명 : Temporal Difference Learning

• Estimate training values for intermediate (non-terminal) board positions by the estimated value of their successor in an actual game trace.

• where successor(b) is the next board position

where it is the program’s move in actual play.

• Values towards the end of the game are initially more accurate and continued training slowly “backs up” accurate values to earlier board positions.

))successor(()( bVbVtrain

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How to learn?

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How to learn?

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How to change the weights?

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How to change the weights?

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Obtaining Training Values

• Direct supervision may be available for the target function.

• With indirect feedback, training values can be estimated using temporal difference learning (used in reinforcement learning where supervision is delayed reward).

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Learning Algorithm

• Uses training values for the target function to induce a hypothesis definition that fits these examples and hopefully generalizes to unseen examples.

• In statistics, learning to approximate a continuous function is called regression.

• Attempts to minimize some measure of error (loss function) such as mean squared error:

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The LMS(Least Mean Square) weight update rule

• Due to mathematical reasoning, the following update rule is very sensible.

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LMS Discussion

• Intuitively, LMS executes the following rules:– 예제를 적용한 결과 (the output for an example ) 가 정확하다면 , 변화를 주지

않는다 . – 예제를 적용한 결과 가 너무 높게 나오면 , 해당 features 의 값에

비례하여 weight 값을 낮춘다 . 그러면 전반적인 예제의 결과도 줄어들게 된다 .

– 예제를 적용한 결과 가 너무 낮게 나오면 , 해당 features 의 값에 비례하여 weight 값을 높인다 . 그러면 전반적인 예제의 결과도 늘어들게 된다 .

• Under the proper weak assumptions, LMS can be proven to eventually converge to a set of weights that minimizes the mean squared error.

)(bV

)(bV

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Lessons Learned about Learning

• Learning 은 ? 선택된 target function 을 근사화 (approximation) 하기

위해 direct or indirect experience 을 사용한다 .

• Function approximation 이란 ?: a space of hypotheses 에서 training data 들에 가장

적합한 가설 (hypotheses) 을 찾아가는 Search 에 해당 됨

• Different learning methods assume different hypothesis

spaces (representation languages) and/or employ different search techniques.

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Various Function Representations

• Numerical functions– Linear regression– Neural networks– Support vector machines

• Symbolic functions– Decision trees– Rules in propositional logic– Rules in first-order predicate logic

• Instance-based functions– Nearest-neighbor– Case-based

• Probabilistic Graphical Models– Naïve Bayes– Bayesian networks– Hidden-Markov Models (HMMs)– Probabilistic Context Free Grammars (PCFGs)– Markov networks

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Various Search Algorithms

• Gradient descent– Perceptron– Backpropagation

• Dynamic Programming– HMM Learning– Probabilistic Context Free Grammars (PCFGs) Learning

• Divide and Conquer– Decision tree induction– Rule learning

• Evolutionary Computation– Genetic Algorithms (GAs)– Genetic Programming (GP)– Neuro-evolution

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Evaluation of Learning Systems

• Experimental– Conduct controlled cross-validation experiments to compare

various methods on a variety of benchmark datasets.– Gather data on their performance, e.g. test accuracy,

training-time, testing-time.– Analyze differences for statistical significance.

• Theoretical– Analyze algorithms mathematically and prove theorems

about their:• Computational complexity• Ability to fit training data• Sample complexity (number of training examples needed to

learn an accurate function)

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Core parts of the machine learning

Many machine learning systems can be usefully characterized in terms of these four generic modules.

(Game history)

(Initial game board)( )^

V

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Four Components of a Learning System (2)

• Generalizer– Input: training example – Output: hypothesis (estimate of the target function)– Generalizes from the specific training examples– Hypothesizes a general function

• Experiment generator– Input - current hypothesis– Output - a new problem– Picks new practice problem maximizing the learning

rate

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Four Components of a Learning System(1)

• Performance system

- Solve the given performance task

- Use the learned target function

- New problem → trace of its solution

• Critic

- Output a set of training examples of the target function

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History of Machine Learning

• 1950s– Samuel’s checker player– Selfridge’s Pandemonium

• 1960s: – Neural networks: Perceptron– Pattern recognition – Learning in the limit theory– Minsky and Papert prove limitations of Perceptron

• 1970s: – Symbolic concept induction– Winston’s arch learner– Expert systems and the knowledge acquisition bottleneck– Quinlan’s ID3– Michalski’s AQ and soybean diagnosis– Scientific discovery with BACON– Mathematical discovery with AM

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History of Machine Learning (cont.)

• 1980s:– Advanced decision tree and rule learning– Explanation-based Learning (EBL)– Learning and planning and problem solving– Utility problem– Analogy– Cognitive architectures– Resurgence of neural networks (connectionism, backpropagation)– Valiant’s PAC Learning Theory– Focus on experimental methodology

• 1990s– Data mining– Adaptive software agents and web applications– Text learning– Reinforcement learning (RL)– Inductive Logic Programming (ILP)– Ensembles: Bagging, Boosting, and Stacking– Bayes Net learning

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History of Machine Learning (cont.)

• 2000s– Support vector machines– Kernel methods– Graphical models– Statistical relational learning– Transfer learning– Sequence labeling– Collective classification and structured outputs– Computer Systems Applications

• Compilers• Debugging• Graphics• Security (intrusion, virus, and worm detection)

– E mail management– Personalized assistants that learn– Learning in robotics and vision

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Remind

• “Learning as search in a space of possible hypotheses”

• Learning methods are characterized by their search strategies and by the underlying structure of the search spaces.

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Issues in Machine Learning

• 특정 학습예제에 대한 general target function 의 학습 가능한 알고리즘 개발

• 훈련용 data 는 얼마나 되어야 하는가 ?

• 학습에 대해 지식을 가지고 있다면 인간의 간여가 도움이 되는가 ?

• 각 훈련 단계별로 학습문제의 복잡도 (Complexity) 를 개선하는 문제

• Function approximation 을 위해 학습의 노력을 줄이는 것

• Target function 의 표현을 자동으로 개선 혹은 변경하는 것