Machine Learning Basics 1. General Introduction

1. Machine Learning Basics1. General Introduction Cong Li Sept. 13th ~ 20th, 2008

2. Outline • Artificial Intelligence • Machine Learning: Modern Approaches to Artificial Intelligence • Machine Learning Problems • Machine Learning Resources • Our Course • Artificial Intelligence • Machine Learning: Modern Approaches to Artificial Intelligence • Machine Learning Problems • Machine Learning Resources • Our Course Machine Learning Basics: 1. General Introduction

3. Intelligence • Intelligence • Ability to solve problems • Examples of Intelligent Behaviors or Tasks • Classification of texts based on content • Heart disease diagnosis • Chess playing Machine Learning Basics: 1. General Introduction

4. Example 1: Text Classification (1) Huge oil platforms dot the Gulf like beacons -- usually lit up like Christmas trees at night. One of them, sitting astride the Rostam offshore oilfield, was all but blown out of the water by U.S. Warships on Monday. The Iranian platform, an unsightly mass of steel and concrete, was a three-tier structure rising 200 feet (60 metres) above the warm waters of the Gulf until four U.S. Destroyers pumped some … Human Judgment Crude Ship Machine Learning Basics: 1. General Introduction

5. Example 1: Text Classification (2) The Federal Reserve is expected to enter the government securities market to supply reserves to the banking system via system repurchase agreements, economists said. Most economists said the Fed would execute three-day system repurchases to meet a substantial need to add reserves in the current maintenance period, although some said a more … Human Judgment Money-fx Machine Learning Basics: 1. General Introduction

6. Example 2: Disease Diagnosis (1) Patient 1’s data Age: 67 Sex: male Chest pain type: asymptomatic Resting blood pressure: 160mm Hg Serum cholestoral: 286mg/dl Fasting blood sugar: < 120mg/dl … Doctor Diagnosis Presence Machine Learning Basics: 1. General Introduction

7. Example 2: Disease Diagnosis (2) Patient 2‘s data Age: 63 Sex: male Chest pain type: typical angina Resting blood pressure: 145mm Hg Serum cholestoral: 233mg/dl Fasting blood sugar: > 120mg/dl … Doctor Diagnosis Absence Machine Learning Basics: 1. General Introduction

8. Example 3: Chess Playing • Chess Game • Two players playing one-by-one under the restriction of a certain rule • Characteristics • To achieve a goal: win the game • Interactive Machine Learning Basics: 1. General Introduction

9. Artificial Intelligence • Artificial Intelligence • Ability of machines in conducting intelligent tasks • Intelligent Programs • Programs conducting specific intelligent tasks Intelligent Processing Input Output Machine Learning Basics: 1. General Introduction

10. Example 1: Text Classifier (1) … fiber = 0 … huge = 1 … oil = 1 platforms = 1 … … Crude = 1 … Money-fx = 0 … Ship = 1 … Text File: Huge oil platforms dot the Gulf like beacons -- usually lit up … Preprocessing Classification Machine Learning Basics: 1. General Introduction

11. Example 1: Text Classifier (2) … enter = 1 expected = 1 … federal = 1 … oil = 0 … … Crude = 0 … Money-fx = 1 … Ship = 0 … Text File: The Federal Reserve is expected to enter the government … Preprocessing Classification Machine Learning Basics: 1. General Introduction

12. Example 2: Disease Classifier (1) Preprocessed data of patient 1 Age = 67 Sex = 1 Chest pain type = 4 Resting blood pressure = 160 Serum cholestoral = 286 Fasting blood sugar = 0 … Classification Presence = 1 Machine Learning Basics: 1. General Introduction

13. Example 2: Disease Classifier (2) Preprocessed data of patient 2 Age = 63 Sex = 1 Chest pain type = 1 Resting blood pressure = 145 Serum cholestoral = 233 Fasting blood sugar = 1 … Classification Presence = 0 Machine Learning Basics: 1. General Introduction

14. Example 3: Chess Program Searching and evaluating Matrix representing the current board Best move -New matrix Opponent’s playing his move Machine Learning Basics: 1. General Introduction

15. AI Approach • Reasoning with Knowledge • Knowledge base • Reasoning • Traditional Approaches • Handcrafted knowledge base • Complex reasoning process • Disadvantages • Knowledge acquisition bottleneck Machine Learning Basics: 1. General Introduction

16. Outline • Artificial Intelligence • Machine Learning: Modern Approaches to Artificial Intelligence • Machine Learning Problems • Research and Resources • Our Course Machine Learning Basics: 1. General Introduction

17. Machine Learning • Machine Learning (Mitchell 1997) • Learn from past experiences • Improve the performances of intelligent programs • Definitions (Mitchell 1997) • 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 the tasks improves with the experiences Machine Learning Basics: 1. General Introduction

18. Example 1: Text Classification Classified text files Text file 1 trade Text file 2ship … … Training Text classifier New text file class Machine Learning Basics: 1. General Introduction

19. Example 2: Disease Diagnosis Database of medical records Patient 1’s data Absence Patient 2’s data Presence … … Training Disease classifier New patient’s data Presence or absence Machine Learning Basics: 1. General Introduction

20. Example 3: Chess Playing Games played: Game 1’s move list Win Game 2’s move list Lose … … Training New matrix representing the current board Strategy of Searching and Evaluating Best move Machine Learning Basics: 1. General Introduction

21. Examples • Text Classification • Task T • Assigning texts to a set of predefined categories • Performance measure P • Precision and recall of each category • Training experiences E • A database of texts with their corresponding categories • How about Disease Diagnosis? • How about Chess Playing? Machine Learning Basics: 1. General Introduction

22. Why Machine Learning Is Possible? • Mass Storage • More data available • Higher Performance of Computer • Larger memory in handling the data • Greater computational power for calculating and even online learning Machine Learning Basics: 1. General Introduction

23. Advantages • Alleviate Knowledge Acquisition Bottleneck • Does not require knowledge engineers • Scalable in constructing knowledge base • Adaptive • Adaptive to the changing conditions • Easy in migrating to new domains Machine Learning Basics: 1. General Introduction

24. Success of Machine Learning • Almost All the Learning Algorithms • Text classification (Dumais et al. 1998) • Gene or protein classification optionally with feature engineering (Bhaskar et al. 2006) • Reinforcement Learning • Backgammon (Tesauro 1995) • Learning of Sequence Labeling • Speech recognition (Lee 1989) • Part-of-speech tagging (Church 1988) Machine Learning Basics: 1. General Introduction

25. Outline • Artificial Intelligence • Machine Learning: Modern Approaches to Artificial Intelligence • Machine Learning Problems • Machine Learning Resources • Our Course Machine Learning Basics: 1. General Introduction

26. Choosing the Training Experience • Choosing the Training Experience • Sometimes straightforward • Text classification, disease diagnosis • Sometimes not so straightforward • Chess playing • Other Attributes • How the training experience is controlled by the learner? • How the training experience represents the situations in which the performance of the program is measured? Machine Learning Basics: 1. General Introduction

27. Choosing the Target Function • Choosing the Target Function • What type of knowledge will be learned? • How it will be used by the program? • Reducing the Learning Problem • From the problem of improving performance P at task T with experience E • To the problem of learning some particular target functions Machine Learning Basics: 1. General Introduction

28. Solving Real World Problems • What Is the Input? • Features representing the real world data • What Is the Output? • Predictions or decisions to be made • What Is the Intelligent Program? • Types of classifiers, value functions, etc. • How to Learn from experience? • Learning algorithms Machine Learning Basics: 1. General Introduction

29. Feature Engineering • Representation of the Real World Data • Features: data’s attributes which may be useful in prediction • Feature Transformation and Selection • Select a subset of the features • Construct new features, e.g. • Discretization of real value features • Combinations of existing features • Post Processing to Fit the Classifier • Does not change the nature Machine Learning Basics: 1. General Introduction

30. Intelligent Programs • Value Functions • Input: features • Output: value • Classifiers (Most Commonly Used) • Input: features • Output: a single decision • Sequence Labeling • Input: sequence of features • Output: sequence of decisions Machine Learning Basics: 1. General Introduction

31. Examples of Value Functions • Linear Regression • Input: feature vectors • Output: • Logistic Regression • Input: feature vectors • Output: Machine Learning Basics: 1. General Introduction

32. Examples of Classifiers • Linear Classifier • Input: feature vectors • Output: • Rule Classifier • Decision tree • A tree with nodes representing condition testing and leaves representing classes • Decision list • If condition 1 then class 1 elseif condition 2 then class 2 elseif …. Machine Learning Basics: 1. General Introduction

33. Examples of Learning Algorithms • Parametric Functions or Classifiers • Given parameters of the functions or classifier, e.g. • Linear functions or classifiers: w, b • Estimating the parameters, e.g. • Loss function optimization • Rule Learning • Condition construction • Rules induction using divide-and-conquer Machine Learning Basics: 1. General Introduction

34. Machine Learning Problems • Methodology of Machine Learning • General methods for machine learning • Investigate which method is better under some certain conditions • Application of Machine Learning • Specific application of machine learning methods • Investigate which feature, classifier, method should be used to solve a certain problem Machine Learning Basics: 1. General Introduction

35. Methodology • Theoretical • Mathematical analysis of performances of learning algorithms (usually with assumptions) • Empirical • Demonstrate the empirical results of learning algorithms on datasets (benchmarks or real world applications) Machine Learning Basics: 1. General Introduction

36. Application • Adaptation of Learning Algorithms • Directly apply, or tailor learning algorithms to specific application • Generalization • Generalize the problems and methods in the specific application to more general cases Machine Learning Basics: 1. General Introduction

37. Outline • Artificial Intelligence • Machine Learning: Modern Approaches to Artificial Intelligence • Machine Learning Problems • Machine Learning Resources • Our Course Machine Learning Basics: 1. General Introduction

38. Introduction Materials • Text Books • T. Mitchell (1997). Machine Learning, McGraw-Hill Publishers. • N. Nilsson (1996). Introduction to Machine Learning (drafts). • Lecture Notes • T. Mitchell’s Slides • Introduction to Machine Learning Machine Learning Basics: 1. General Introduction

39. Technical Papers • Journals, e.g. • Machine Learning, Kluwer Academic Publishers. • Journal of Machine Learning Research, MIT Press. • Conferences, e.g. • International Conference on Machine Learning (ICML) • Neural Information Processing Systems (NIPS) Machine Learning Basics: 1. General Introduction

40. Others • Data Sets • UCI Machine Learning Repository • Reuters data set for text classification • Related Areas • Artificial intelligence • Knowledge discovery and data mining • Statistics • Operation research • … Machine Learning Basics: 1. General Introduction

41. Outline • Artificial Intelligence • Machine Learning: Modern Approaches to Artificial Intelligence • Machine Learning Problems • Machine Learning Resources • Our Course Machine Learning Basics: 1. General Introduction

42. What I will Talk about • Machine Learning Methods • Simple methods • Effective methods (state of the art) • Method Details • Ideas • Assumptions • Intuitive interpretations Machine Learning Basics: 1. General Introduction

43. What I won’t Talk about • Machine Learning Methods • Classical, but complex and not effective methods (e.g., complex neural networks) • Methods not widely used • Method Details • Theoretical justification Machine Learning Basics: 1. General Introduction

44. What You will Learn • Machine Learning Basics • Methods • Data • Assumptions • Ideas • Others • Problem solving techniques • Extensive knowledge of modern techniques Machine Learning Basics: 1. General Introduction

45. References • H. Bhaskar, D. Hoyle, and S. Singh (2006). Machine Learning: a Brief Survey and Recommendations for Practitioners. Computers in Biology and Medicine, 36(10), 1104-1125. • K. Church (1988). A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Texts. In Proc. ANLP-1988, 136-143. • S. Dumais, J. Platt, D. Heckerman and M. Sahami (1998). Inductive Learning Algorithms and Representations for Text Categorization. In Proc. CIKM-1998, 148-155. • K. Lee (1989). Automatic Speech Recognition: The Development of the Sphinx System, Kluwer Academic Publishers. • T. Mitchell (1997). Machine Learning, McGraw-Hill Publishers. • G. Tesauro (1995). Temporal Difference Learning and TD-gammon. Communications of the ACM, 38(3), 58-68. Machine Learning Basics: 1. General Introduction

46. The End