Machine Learning Basics 1. General Introduction
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Transcript of Machine Learning Basics 1. General Introduction
Machine Learning Basics1. General Introduction
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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
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
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
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
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
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
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
Artificial Intelligence
Artificial Intelligence Ability of machines in conducting
intelligent tasks Intelligent Programs
Programs conducting specific intelligent tasks
Input
Intelligent Processing
Output
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
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
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
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
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
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
Outline
Artificial Intelligence Machine Learning: Modern
Approaches to Artificial Intelligence
Machine Learning Problems Research and Resources Our Course
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
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
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
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
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
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
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
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
Outline
Artificial Intelligence Machine Learning: Modern
Approaches to Artificial Intelligence
Machine Learning Problems Machine Learning Resources Our Course
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?
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
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
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
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
Examples of Value Functions
Linear Regression Input: feature vectors Output:
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1
)( xwx
),,,( 21 nxxx x
bef
xw
x1
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Logistic Regression Input: feature vectors Output:
Machine Learning Basics: 1. General Introduction
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 ….
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
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
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
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
Outline
Artificial Intelligence Machine Learning: Modern
Approaches to Artificial Intelligence
Machine Learning Problems Machine Learning Resources Our Course
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
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
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
Outline
Artificial Intelligence Machine Learning: Modern
Approaches to Artificial Intelligence
Machine Learning Problems Machine Learning Resources Our Course
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
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
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
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.
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