1. Machine Learning Introduction

2. 2 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

3. 3 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

4. 기계학습과 통계처리  기계학습과 통계 처리의 차이 – 기계학습 : 데이터에서 일반 규칙을 뽑아 개별 데이터 ( 사건 ) 에 적용 – 통계 : 데이터에서 일어나는 평균적 현상을 개량적으로 평가하 여 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 4

5. 5 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

6. 6 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

7. 7 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.

8. 8 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?

9. 9 Why Machine Learning  Recent progress in algorithms and theory  Growing flood of online data  Computational power is available  Budding industry

10. 10 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

11. 11 Typical Datamining Task (1/2)  Data :

12. 12 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

13. 13 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

14. 14 Credit Risk Analysis (1/2)  Data :

15. 15 Credit Risk Analysis (2/2) Rules learned from synthesized data: If Other-Delinquent-Accounts > 2, and Number-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]

16. 16 Other Prediction Problems (1/2)

17. 17 Other Prediction Problems (2/2)

18. 18 Problems Too Difficult to Program by Hand  ALVINN [Pomerleau] drives 70 mph on highways

19. 19 Software that Customizes to User http://www.wisewire.com

20. 20 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

21. 21 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?

22. 22 Relevant Disciplines  Artificial intelligence  Bayesian methods  Computational complexity theory  Control theory  Information theory  Philosophy  Psychology and neurobiology  Statistics ...

23. 23 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

24. 24 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?

25. 25 Type of Training Experience  Direct or indirect?  Teacher or not? A problem: is training experience representative of performance goal?

26. 26 Choose the Target Function  ChooseMove : Board  Move ??  V : Board  R ?? ...

27. 27 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

28. 28 Choose Representation for Target Function  collection of rules?  neural network ?  polynomial function of board features? ...

29. 29 A Representation for Learned Function w 0 + w 1 ·bp(b)+w 2 ·rp(b)+w 3 ·bk(b)+w 4 ·rk(b)+w 5 ·bt(b)+w 6 ·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

30. 30 Obtaining Training Examples  V(b): the true target function  V(b) : the learned function  V train (b): the training value One rule for estimating training values:  V train (b)  V(Successor(b)) ^ ^

31. 31 Choose Weight Tuning Rule LMS Weight update rule: Do repeatedly:  Select a training example b at random 1. Computeerror(b): error(b) = V train (b) – V(b) 2. For each board feature f i, update weight w i : w i  w i + c · f i · error(b) c is some small constant, say 0.1, to moderate the rate of learning

32. 32 Final design  The performance system –Playing games  The critic – 차이 발견 ( 분석 )  The generalizer –Generate new hypothesis  The experiment generator –Generate new problems

33. 33 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 적자생 존을 통해 진화 –Explanation-based learning ::: 이기고 지는 이유에 대 한 분석을 통한 학습

34. 34 Design Choices

35. 35 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?