Machine Learning

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
 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 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

7. Machine Learning Basics: 1. General Introduction
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

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
 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
Opponent’s
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 patient’s
data
Presence or
absence
Database of medical records
Patient 1’s data Absence
Patient 2’s 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 1’s move list Win
Game 2’s 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
 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
 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: 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

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:
),,,( 21 nxxx x


n
i
ii bxwbf
1
)( xwx
),,,( 21 nxxx x
b
e
f 

 xw
x
1
1
)(
 Logistic Regression
 Input: feature vectors
 Output:

32. Machine Learning Basics: 1. General Introduction
Examples of Classifiers
 Linear Classifier
 Input: feature vectors
 Output:
),,,( 21 nxxx x
)sgn()sgn(
1


n
i
ii bxwby xw
 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).
 Lecture Notes
 T. Mitchell’s 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
 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 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

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),
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.

46. The End