Machine Learning Introduction. 2 교재  Machine Learning, Tom T. Mitchell, McGraw- Hill  일부  Reinforcement Learning: An Introduction, R. S. Sutton and.

Slides:



Advertisements
Similar presentations
Machine learning Overview
Advertisements

Statistical Machine Learning- The Basic Approach and Current Research Challenges Shai Ben-David CS497 February, 2007.
1 Machine Learning: Lecture 1 Overview of Machine Learning (Based on Chapter 1 of Mitchell T.., Machine Learning, 1997)
1 Machine Learning Spring 2010 Rong Jin. 2 CSE847 Machine Learning Instructor: Rong Jin Office Hour: Tuesday 4:00pm-5:00pm Thursday 4:00pm-5:00pm Textbook.
Godfather to the Singularity
1er. Escuela Red ProTIC - Tandil, de Abril, 2006 Introduction to Machine Learning Alejandro Ceccatto Instituto de Física Rosario CONICET-UNR.
Kansas State University Department of Computing and Information Sciences CIS 798: Intelligent Systems and Machine Learning Tuesday, August 24, 1999 William.
CS 484 – Artificial Intelligence1 Announcements Project 1 is due Tuesday, October 16 Send me the name of your konane bot Midterm is Thursday, October 18.
1er. Escuela Red ProTIC - Tandil, de Abril, Introduction How to program computers to learn? Learning: Improving automatically with experience.
1 Machine Learning Spring 2013 Rong Jin. 2 CSE847 Machine Learning  Instructor: Rong Jin  Office Hour: Tuesday 4:00pm-5:00pm TA, Qiaozi Gao, Thursday.
CII504 Intelligent Engine © 2005 Irfan Subakti Department of Informatics Institute Technology of Sepuluh Nopember Surabaya - Indonesia.
Machine Learning Case study. What is ML ?  The goal of machine learning is to build computer systems that can adapt and learn from their experience.”
An Introduction to Machine Learning In the area of AI (earlier) machine learning took a back seat to Expert Systems Expert system development usually consists.
Machine Learning Bob Durrant School of Computer Science
Machine Learning (Extended) Dr. Ata Kaban
1 Machine Learning Spring 2010 Rong Jin. 2 CSE847 Machine Learning  Instructor: Rong Jin  Office Hour: Tuesday 4:00pm-5:00pm Thursday 4:00pm-5:00pm.
Machine Learning Group University College Dublin 4.30 Machine Learning Pádraig Cunningham.
Machine Learning: Foundations Yishay Mansour Tel-Aviv University.
Carla P. Gomes CS4700 CS 4700: Foundations of Artificial Intelligence Prof. Carla P. Gomes Module: Intro Neural Networks (Reading:
Machine Learning (ML) and Knowledge Discovery in Databases (KDD)
Machine Learning CSE 473. © Daniel S. Weld Topics Agency Problem Spaces Search Knowledge Representation Reinforcement Learning InferencePlanning.
1 Some rules  No make-up exams ! If you miss with an official excuse, you get average of your scores in the other exams – at most once.  WP only-if you.
A Brief Survey of Machine Learning
CS157A Spring 05 Data Mining Professor Sin-Min Lee.
Introduction to machine learning
Data Mining By Andrie Suherman. Agenda Introduction Major Elements Steps/ Processes Tools used for data mining Advantages and Disadvantages.
CS Machine Learning. What is Machine Learning? Adapt to / learn from data  To optimize a performance function Can be used to:  Extract knowledge.
DATA MINING : CLASSIFICATION. Classification : Definition  Classification is a supervised learning.  Uses training sets which has correct answers (class.
Data Mining Joyeeta Dutta-Moscato July 10, Wherever we have large amounts of data, we have the need for building systems capable of learning information.
Introduction to Machine Learning MSE 2400 EaLiCaRA Spring 2015 Dr. Tom Way Based in part on notes from Gavin Brown, University of Manchester.
Short Introduction to Machine Learning Instructor: Rada Mihalcea.
CpSc 810: Machine Learning Design a learning system.
CpSc 881: Machine Learning Introduction. 2 Copy Right Notice Most slides in this presentation are adopted from slides of text book and various sources.
Computing & Information Sciences Kansas State University Wednesday, 13 Sep 2006CIS 490 / 730: Artificial Intelligence Lecture 9 of 42 Wednesday, 13 September.
Machine Learning Introduction Study on the Coursera All Right Reserved : Andrew Ng Lecturer:Much Database Lab of Xiamen University Aug 12,2014.
Computing & Information Sciences Kansas State University Lecture 10 of 42 CIS 530 / 730 Artificial Intelligence Lecture 10 of 42 William H. Hsu Department.
Lecture 10: 8/6/1435 Machine Learning Lecturer/ Kawther Abas 363CS – Artificial Intelligence.
Introduction Introduction Dr. Khaled Wassif Spring Machine Learning.
1 Machine Learning (Extended) Dr. Ata Kaban Algorithms to enable computers to learn –Learning = ability to improve performance automatically through experience.
Machine Learning (ML) and Knowledge Discovery in Databases (KDD) Instructor: Rich Maclin Texts: Machine Learning, Mitchell Notes based.
Well Posed Learning Problems Must identify the following 3 features –Learning Task: the thing you want to learn. –Performance measure: must know when you.
1 Machine Learning 1.Where does machine learning fit in computer science? 2.What is machine learning? 3.Where can machine learning be applied? 4.Should.
Computing & Information Sciences Kansas State University Wednesday, 12 Sep 2007CIS 530 / 730: Artificial Intelligence Lecture 9 of 42 Wednesday, 12 September.
Learning from observations
Machine Learning, Decision Trees, Overfitting Machine Learning Tom M. Mitchell Machine Learning Department Carnegie Mellon University January 14,
Data Mining BY JEMINI ISLAM. Data Mining Outline: What is data mining? Why use data mining? How does data mining work The process of data mining Tools.
Kansas State University Department of Computing and Information Sciences CIS 730: Introduction to Artificial Intelligence Lecture 9 of 42 Wednesday, 14.
Chapter 1: Introduction. 2 목 차목 차 t Definition and Applications of Machine t Designing a Learning System  Choosing the Training Experience  Choosing.
Machine Learning Introduction. Class Info Office Hours –Monday:11:30 – 1:00 –Wednesday:10:00 – 1:00 –Thursday:11:30 – 1:00 Course Text –Tom Mitchell:
يادگيري ماشين Machine Learning Lecturer: A. Rabiee
Data Mining and Decision Support
1 Introduction to Machine Learning Chapter 1. cont.
Introduction Machine Learning: Chapter 1. Contents Types of learning Applications of machine learning Disciplines related with machine learning Well-posed.
Introduction to Machine Learning © Roni Rosenfeld,
Well Posed Learning Problems Must identify the following 3 features –Learning Task: the thing you want to learn. –Performance measure: must know when you.
Computing & Information Sciences Kansas State University CIS 530 / 730: Artificial Intelligence Lecture 09 of 42 Wednesday, 17 September 2008 William H.
Machine Learning BY UZMA TUFAIL MCS : section (E) ROLL NO: /31/2016.
BAYESIAN LEARNING. 2 Bayesian Classifiers Bayesian classifiers are statistical classifiers, and are based on Bayes theorem They can calculate the probability.
Machine Learning & Datamining CSE 454. © Daniel S. Weld 2 Project Part 1 Feedback Serialization Java Supplied vs. Manual.
Network Management Lecture 13. MACHINE LEARNING TECHNIQUES 2 Dr. Atiq Ahmed Université de Balouchistan.
1 Machine Learning Patricia J Riddle Computer Science 367 6/26/2016Machine Learning.
Machine Learning. Definition: The ability of a machine to improve its performance based on previous results.
Supervise Learning Introduction. What is Learning Problem Learning = Improving with experience at some task – Improve over task T, – With respect to performance.
Introduction to Machine Learning, its potential usage in network area,
School of Computer Science & Engineering
Spring 2003 Dr. Susan Bridges
3.1.1 Introduction to Machine Learning
Why Machine Learning Flood of data
Machine Learning (ML) and Knowledge Discovery in Databases (KDD)
CAP 5610: Introduction to Machine Learning Spring 2011 Dr
Presentation transcript:

Machine Learning Introduction

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 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 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 예제 프로그램  –Face recognition –Decision tree learning code –Data for financial loan analysis –Bayes classifier code –Data for analyzing text documents

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

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 Typical Datamining Task (1/2)  Data :

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 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 Credit Risk Analysis (1/2)  Data :

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 Other Prediction Problems (1/2)

17 Other Prediction Problems (2/2)

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

19 Software that Customizes to User

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

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

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

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

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

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 Design Choices

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?