1. Data Mining & Machine Learning Introduction
Intelligent Systems Lab.
Soongsil University
Thanks to Raymond J. Mooney in the University of Texas at Austin, Isabelle Guyon

2. Artificial Intelligence (AI): Research Areas
Learning Algorithms
Inference Mechanisms
Knowledge Representation
Intelligent System Architecture
Research
Intelligent Agents
Information Retrieval
Electronic Commerce
Data Mining
Bioinformatics
Natural Language Proc.
Expert Systems
Artificial
Intelligence
Application
Rationalism (Logical)
Empiricism (Statistical)
Connectionism (Neural)
Evolutionary (Genetic)
Biological (Molecular)
Paradigm

3. Artificial Intelligence (AI): Paradigms
Symbolic AI
Rule-Based Systems
Connectionist AI
Neural Networks
Evolutionary AI
Genetic Algorithms
Molecular AI:
DNA Computing

4. What is Machine Learning?
algorithm
Trained
machine
TRAINING
DATA
Answer ?
Query

5. Definition of learning
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
Task, T
Experience, E
Task
Learned
Program
Learning Program
Program
Performance
Performance, P

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

7. Machine Learning Supervised Learning Unsupervised 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

8. 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

9. 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

10. Trends leading to Data Flood
More data is generated:
Bank, telecom, other business transactions ...
Scientific data: astronomy, biology, etc
Web, text, and e-commerce

11. 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

12. 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): ?

13. Data Growth
In 2 years, the size of the largest database TRIPLED!

14. 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, …

15. Data Mining for Customer Modeling
Customer Tasks:
attrition prediction
targeted marketing:
cross-sell, customer acquisition
credit-risk
fraud detection
Industries
banking, telecom, retail sales, …

16. 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.

17. 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.

18. 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)

19. 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

20. 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

21. 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

22. 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

23. 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

24. Chess Game In 1997, Deep Blue(IBM) beat Garry Kasparov(러).
– Let IBM’s stock increase
by $18 billion at that year

25. Some Successful Applications of Machine 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

26. Some Successful Applications of Machine Learning
Learning to recognize spoken words
– Speech recognition/synthesis
– Natural language understanding/generation
– Machine translation

27. 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..)

28. Face Recognition - 1
Given multiple angles/ views of a person, learn to identify them.
Learn to distinguish
male from female faces.

29. Face Recognition - 2 Learn to recongnize emotions, gestures
Li, Ye, Kambhametta, 2003

30. 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

31. • Honda ASIMO (Advanced Step in Innovate MObility)
Robot
• Honda ASIMO (Advanced Step in Innovate MObility)
– Born on 31 October, 2001
– Height: 120 CM, Weight: 52 KG

32. Biomedical / Biometrics
Medicine:
Screening
Diagnosis and prognosis
Drug discovery
Security:
Face recognition
Signature / fingerprint
DNA fingerprinting

33. Computer / Internet Computer interfaces: Internet
Troubleshooting wizards
Handwriting and speech
Brain waves
Internet
Spam filtering
Text categorization
Text translation
Recommendation 7

34. 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
Recommended articles in a newspaper
Recommended books, movies, music, or jokes
Financial investments
DNA sequences
Spoken words
Handwritten letters
Astronomical images

35. 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

36. Applications training examples Bioinformatics inputs Market Analysis10
102
103
104
105
Bioinformatics
Ecology
OCR
HWR
Market
Analysis
Text
Categorization
Machine
Vision
System diagnosis

37. 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

38. Disciplines Related with Machine Learning (2)
Information theory
Entropy와 Information Content를 측정, Minimum Description Length, Optimal Code 와 Optimal Training의 관계
Philosophy
Occam’s Razor, 일반화의 타당성 분석
Psychology and neurobiology
Neural network models
Statistics
가설의 정확도 추정시 발생하는 에러의 특성화, 신뢰구간, 통계적 검증

39. Definition of learning
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
Task, T
Experience, E
Task
Learned
Program
Learning Program
Program
Performance
Performance, P

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

41. 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.

42. 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.

43. 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?

44. Measuring Performance
Classification Accuracy
Solution correctness
Solution quality (length, efficiency)
Speed of performance

45. 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.
Train examples…
Learner
Environment/
Experience
Knowledge
Performance
Element
Test examples…

46. 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
Learner의 자율성, 학습 정보를 얻을 때 teacher의 도움을 받는 정도,
Distribution of examples
Train examples의 분포와 Test examples의 분포의 문제
시스템의 성능을 평가하는 테스트의 예제 분포를 잘 반영해야 함
특수한 경우의 분포가 반영 (The Checkers World Champion이 격은 특수한 경우의 분포가 반영 될까 ?)
Degree of controlling the sequence ..:
Learner는 보드의 상태를 선택하고 정확한 움직임 제시하기 위해 teacher에게 의존 할 수 있을 것이다.
그렇지 않으면, Learner 자신이 좀 혼란스러운 보드상태를 제시하고 정확한 움직임을 물을 수도 있다. 혹은 Learner는 teacher의 도움 없이 보드상태와 학습을 완성 할 수 있다. 처음 접하고 아직 생각하지 못한 것을 실험해보는 것과 현재까지 가장 유리한 상태의 것을 약간 변형하는 정도로 기량을 연마하는 것 사이에 선택을 할 수 있다. Learner는 teacher에게 다양한 질의를 하고 자체적으로 학습 예제들을 선택하도록 하는 것이 좋다.

47. 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?

48. 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)

49. 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 network에 대한 관계 모델구축 시)
If test distribution is different, requires transfer learning. that is, achieving cumulative learning
Collective classification refers to the combined classification of a set of interlinked ... search in collective classification has been devoted to the development of ...
Semi-supervised learning !!

51. Transfer learning

52. 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.

53. 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.
Learner
Environment/
Experience
Knowledge
Performance
Element

54. Designing a Learning System
2. Choosing a Target Function
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.
* V는 각각의 움직임에 대한 결과의 점수에 따라 선택하는데 이용가능 하여
* 결국은 최고 높은 점수를 얻을 수 있는 움직임을 선택하게 한다.

55. Designing a Learning System
2. Choosing the Target Function
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

56. 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 Position에서도 그 Position이 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) =

57. 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 Position에서도 그 Position이 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!

58. 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.

59. 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.
Learner
Environment/
Experience
Knowledge
Performance
Element

60. 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.

61. Approximate representation
w1 - w6: weights

62. 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

63. 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.
Learner
Environment/
Experience
Knowledge
Performance
Element

64. 4. Choosing a Function Approximation Algorithm
A training example is represented as an ordered pair
<b, Vtrain(b) >
b: board state
Vtrain(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 value은 ← 다음단계의 장기판 상태(b+1)의 가설의 함수값을 사용

65. DESIGNING A LEARNING SYSTEM
Estimating Training Values

66. 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.

67. How to learn?

68. How to learn?

69. How to change the weights?

70. How to change the weights?

71. 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).

72. 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:

73. The LMS(Least Mean Square) weight update rule
Due to mathematical reasoning, the following update rule is very sensible.

74. 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 features의 값에 비례하여 weight값을 낮춘다. 그러면 전반적인 예제의 결과도 줄어들게 된다.
예제를 적용한 결과 가 너무 낮게 나오면, 해당 features의 값에 비례하여 weight값을 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.

75. Lessons Learned about Learning
선택된 target function을 근사화(approximation) 하기 위해 direct or indirect experience을 사용한다.
Function approximation 이란 ?:
a space of hypotheses에서 training data들에 가장 적합한 가설(hypotheses)을 찾아가는 Search에 해당 됨
Different learning methods assume different hypothesis spaces (representation languages) and/or employ different search techniques.

76. 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

77. 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

78. 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)

79. Core parts of the machine learning
( )
(Initial game board)
(Game history)
Many machine learning systems can be usefully characterized in terms of these four generic modules.

80. 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

81. 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

82. History of Machine Learning
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

83. History of Machine Learning (cont.)
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

84. History of Machine Learning (cont.)
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

85. 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.

86. Issues in Machine Learning
특정 학습예제에 대한 general target function의 학습 가능한 알고리즘 개발
훈련용 data는 얼마나 되어야 하는가 ?
학습에 대해 지식을 가지고 있다면 인간의 간여가 도움이 되는가 ?
각 훈련 단계별로 학습문제의 복잡도(Complexity)를 개선하는 문제
Function approximation을 위해 학습의 노력을 줄이는 것
Target function의 표현을 자동으로 개선 혹은 변경하는 것