Transcript Machine Learning

Machine Learning
Defining Questions
• The field of Machine Learning seeks to answer central
question
“How can we build computer systems that
automatically
improve with experience? and how can we build machines
that solve problems?”
• Question covers a broad range of learning tasks, how to
data mine historical medical records to learn which
future patients will respond best to which treatments,
and how to build search engines that automatically
customize to their user’s interests.
Machine learning
• Machine Learning is an area within Artificial
Intelligence concerned with how a computational
system can acquire knowledge and implement it by
learning from its experiences and observations.
• Machine learning is concerned with the development
and analysis of algorithms and techniques that allow
computers to "learn ”,and automatically improve a
system's performance. Automatic improvement might
include: (1) learning to perform a new task; (2) learning to
perform a task more efficiently or effectively; or (3) learning
and organizing new facts that can be used by a system that
relies upon such knowledge.
Types Of Learning
• At a general level, there are two types of learning:
inductive, and deductive.
• Induction or inductive reasoning, sometimes called
inductive logic, is the process of reasoning in which
the premises of an argument are believed to
support the conclusion but do not ensure it.
• Deductive reasoning is the kind of reasoning in which
the conclusion is necessitated by, or reached from,
previously known facts (the premises). If the
premises are true, the conclusion must be true .
Machine learning
• Machine learning usually refers to the changes in
systems that perform tasks associated with artificial
intelligence (AI). Such tasks involve recognition
,diagnosis, planning, robot control, prediction. The
"changes" might be either enhancements to already
performing systems or from the beginning to synthesis
of new systems.
Algorithm Types
• There are many ways to categorize machine learning
algorithms (Algorithm types) that based on the amount
and type of background information provided to the
algorithm.
• The most important type: 1)supervised learning :where
the algorithm generates a function that maps inputs to
desired outputs. One standard formulation of the
supervised learning task is the classification problem.
• 2)unsupervised learning :which models a set of inputs:
labeled examples are not available.
Areas of Influence for
Machine Learning
• The most important areas:
• Statistics: How best to use samples drawn from
unknown probability distributions to help decide from
which distribution some new sample is drawn?
• Artificial Intelligence: How to write algorithms to
acquire the knowledge humans are able to acquire, at
least, as well as humans?
Why Is Machine
Learning Important?
• Some tasks cannot be defined well, Machine Learning
can help to defined it for example (recognizing
people).
• Relationships and correlations can be hidden within
large amounts of data. Machine Learning/Data Mining
may be able to find these relationships.
• The amount of knowledge available about certain tasks
might be too large for explicit encoding by humans
(e.g., medical diagnostic).
• Environments change over time
Applications
• Machine learning has a wide spectrum of
applications including natural language
processing ,search engines ,medical
diagnosis ,bioinformatics and
cheminformatics ,detecting credit card
fraud ,stock market analysis, classifying
DNA sequences ,speech and handwriting
recognition ,object recognition in computer
vision ,game playing and robot locomotion.
Thanks For listening