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Machine Learning Basics 1. General Introduction Compiled For Ph.D. course Work APSU, Rewa, MP, India.

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Presentation on theme: "Machine Learning Basics 1. General Introduction Compiled For Ph.D. course Work APSU, Rewa, MP, India."— Presentation transcript:

1 Machine Learning Basics 1. General Introduction Compiled For Ph.D. course Work APSU, Rewa, MP, India

2 Machine Learning Basics: 1. General Introduction 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

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

4 Machine Learning Basics: 1. General Introduction 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

5 Machine Learning Basics: 1. General Introduction 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

6 Machine Learning Basics: 1. General Introduction Example 2: Disease Diagnosis (1) Patient 1s 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

7 Machine Learning Basics: 1. General Introduction Example 2: Disease Diagnosis (2) Patient 2s 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

8 Machine Learning Basics: 1. General Introduction 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

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

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

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

12 Machine Learning Basics: 1. General Introduction 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

13 Machine Learning Basics: 1. General Introduction 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

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

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

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

17 Machine Learning Basics: 1. General Introduction 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

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

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

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

21 Machine Learning Basics: 1. General Introduction 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?

22 Machine Learning Basics: 1. General Introduction 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

23 Machine Learning Basics: 1. General Introduction 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

24 Machine Learning Basics: 1. General Introduction 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)

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

26 Machine Learning Basics: 1. General Introduction 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?

27 Machine Learning Basics: 1. General Introduction 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

28 Machine Learning Basics: 1. General Introduction 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

29 Machine Learning Basics: 1. General Introduction Feature Engineering Representation of the Real World Data Features: datas 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

30 Machine Learning Basics: 1. General Introduction 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

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

32 Machine Learning Basics: 1. General Introduction 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 ….

33 Machine Learning Basics: 1. General Introduction 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

34 Machine Learning Basics: 1. General Introduction 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

35 Machine Learning Basics: 1. General Introduction 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)

36 Machine Learning Basics: 1. General Introduction 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

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

38 Machine Learning Basics: 1. General Introduction Introduction Materials Text Books T. Mitchell (1997). Machine Learning, McGraw-Hill Publishers. N. Nilsson (1996). Introduction to Machine Learning (drafts).drafts Lecture Notes T. Mitchells Slides Introduction to Machine Learning

39 Machine Learning Basics: 1. General Introduction 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)

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

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

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

43 Machine Learning Basics: 1. General Introduction What I wont 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

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

45 Machine Learning Basics: 1. General Introduction 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), K. Church (1988). A Stochastic Parts Program and Noun Phrase Parser for Unrestricted Texts. In Proc. ANLP- 1988, S. Dumais, J. Platt, D. Heckerman and M. Sahami (1998). Inductive Learning Algorithms and Representations for Text Categorization. In Proc. CIKM- 1998, 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),

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