Download Applications of Machine Learning

Data Mining and Machine Learning
CompSci 760
Lecture 16
Applications of Machine Learning
The Activity Pyramid
Fielded
Applications
Applied Research
Basic Research
Definition of a Machine Learning System
a software
artifact
by acquiring
knowledge
that improves
task performance
based on partial
task experience
Potential Applications
Machine learning has seen many successful applications over the
past decade, saving or making companies vast sums.
• The most visible applications involve the World Wide Web,
which supports easy collection of data on line.
• Many of these and related applications revolve around targeted
advertising and marketing.
However, machine learning applies to any task that will benefit
from improved performance and for which data are available.
The earliest applications of learning technology covered a broad
spectrum that included industrial settings.
Potential Applications
Decision
Making
Configuration
and Design
Monitoring
and Diagnosis
Language
Processing
Planning and
Scheduling
Execution
and Control
Speech and
Vision
Potential Applications
Decision
Making
Configuration
and Design
Monitoring
and Diagnosis
Language
Processing
Planning and
Scheduling
Classification
and Prediction
Execution
and Control
Speech and
Vision
Stages in Developing Learning Applications
Formulating
the Problem
Engineering the
Representation
Collecting and
Preparing Data
Inducing
the Model
Evaluating the
Model
Fielding and
Acceptance
Stages in Developing Learning Applications
Formulating
the Problem
Engineering the
Representation
Hypothesis: The induction
method matters less than other
other factors in determining if
an application is successful.
Evaluating the
Model
Collecting and
Preparing Data
Inducing
the Model
Fielding and
Acceptance
Machine Learning
Learned Knowledge
If BP > 150 and
HR < 80
Then Give-Diuretic = Yes
If BP > 180 and
HR > 80
Then Give-Diuretic = No
Machine Earning
Memory Cash
$
Improving the Application Process
Most effort in developing fielded
applications of learning concern
the stages before and after the
induction step.
Formulating
the Problem
Engineering the
Representation
To improve the overall process
of application, we must start to
automate these other stages.
Evaluating the
Model
Collecting and
Preparing Data
Inducing
the Model
This requires basic research
on problem formulation,
feature engineering, data
processing, and social factors.
Fielding and
Acceptance
Concluding Remarks
Machine learning has seen many fielded applications that have
provided major commercial or other benefits.
• The vast majority of applications have involved formulating
the task in terms of supervised learning.
• Web-based applications have become popular because data are
readily available.
• Most of the effort in developing an application lies in stages
before and after invoking the induction mechanism.
Making the application process more efficient and effective will
require basic research on automating these other stages.