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

Machine Learning Basics1. General Introduction

Compiled ForPh.D. course Work

APSU, Rewa, MP, India

Machine Learning Basics: 1. General Introduction

Outline

Artificial Intelligence Machine Learning: Modern

Approaches to Artificial Intelligence

Machine Learning Problems Machine Learning Resources Our Course





Intelligence

Intelligence Ability to solve problems

Examples of Intelligent Behaviors or Tasks Classification of texts based on

content Heart disease diagnosis Chess playing


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


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


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


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


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


Artificial Intelligence

Artificial Intelligence Ability of machines in conducting

intelligent tasks Intelligent Programs

Programs conducting specific intelligent tasks

Input

Intelligent Processing

Output


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


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


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


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


Example 3: Chess Program

Best move -New matrix

Opponent’s playing his

move

Matrix representing the current board

Searching and evaluating


AI Approach

Reasoning with Knowledge Knowledge base Reasoning

Traditional Approaches Handcrafted knowledge base Complex reasoning process Disadvantages

Knowledge acquisition bottleneck


Outline



Machine Learning Problems Research and Resources Our Course


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


Example 1: Text Classification

Text classifier

New text file class

Classified text files

Text file 1 trade

Text file 2 ship

… …

Training


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


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


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?


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


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


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)


Outline





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?


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


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


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


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


Examples of Value Functions

Linear Regression Input: feature vectors Output:

),,,( 21 nxxx x

n

iii bxwbf

1

)( xwx

),,,( 21 nxxx x

bef

xw

x1

1)(

Logistic Regression Input: feature vectors Output:


Examples of Classifiers

Linear Classifier Input: feature vectors Output:

),,,( 21 nxxx x

)sgn()sgn(1

n

iii 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 ….


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 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


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)


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


Outline





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


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)


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 …


Outline





What I will Talk about

Machine Learning Methods Simple methods Effective methods (state of the art)

Method Details Ideas Assumptions Intuitive interpretations


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


What You will Learn

Machine Learning Basics Methods Data Assumptions Ideas

Others Problem solving techniques Extensive knowledge of modern

techniques


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.

The End

Documents

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