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
Machine Learning Basics: 1. General Introduction

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
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
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. 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
Machine Learning Basics: 1. General Introduction

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
Machine Learning Basics: 1. General Introduction

17. Machine Learning Basics: 1. General Introduction
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 trade
Text file ship
… …
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. 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?
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. 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
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. Machine Learning Basics: 1. General Introduction
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
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
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. 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
Machine Learning Basics: 1. General Introduction

30. Machine Learning Basics: 1. General Introduction
Intelligent Programs
Value Functions
Input: features
Output: value
Classifiers (Most Commonly Used)
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. 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)
Machine Learning Basics: 1. General Introduction

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
Machine Learning Basics: 1. General Introduction

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
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. 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)
Machine Learning Basics: 1. General Introduction

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 …
Machine Learning Basics: 1. General Introduction

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
Machine Learning Basics: 1. General Introduction

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
Machine Learning Basics: 1. General Introduction

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
Machine Learning Basics: 1. General Introduction

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
Machine Learning Basics: 1. General Introduction

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),
Machine Learning Basics: 1. General Introduction

46. The End