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Presented By:-
Darshan S. Ambhaikar
Sinhgad Institute of Management Pune
TABLE OF
CONTENT
1. Definition
2. What is machine learning
3. Traditional programming and machine learning
4. Why machine learning is important
5. Generalization
6. Machine learning and data mining
7. Algorithms
8. Decision tree learning
9. Other learning techniques
10.Examples
11.Applications
12.Few quotes
13.Question and answers
Introduction
Machine
learning, a
branch of
artificial
intelligence,
concerns the
construction
and study of
systems that
can learn
from data.
•In 1959, Arthur Samuel defined machine learning as a
"Field of study that gives computers the ability to learn
without being explicitly programmed".
•“The goal of machine learning is to build computer
systems that can adapt and learn from their experience.”
Tom Dietterich
•Tom M. Mitchell provided a widely quoted, more
formal definition: "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 tasks
in T, as measured by P, improves with experience E"
Definition Machine Learning
So What Is Machine Learning?
•Automating automation
•Getting computers to program
themselves
•Writing software is the
bottleneck
Let the data do the work instead!
Traditional Programming
Machine Learning
Computer
Data
Program
Output
Computer
Data
Output
Program
Magic?
No, more like
gardening
• Seeds
• Nutrients
• Gardener
• Plants
= Algorithms
= Data
= You
= Programs
Why Machine Learning is Important
•Some tasks cannot be defined well, except
by examples (e.g., recognizing people).
•Relationships and correlations can be
hidden within large amounts of data.
Machine Learning/Data Mining may be able
to find these relationships.
•Human designers often produce machines
that do not work as well as desired in the
environments in which they are used.
•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.
•New knowledge about tasks is constantly being
discovered by humans. It may be difficult to
continuously re-design systems “by hand”.
Why is Machine Learning Important
(Cont’d)?
•A core objective of a learner is to generalize from
its experience. Generalization in this context is the
ability of a learning machine to perform
accurately on new, unseen examples/tasks after
having experienced a learning data set.
Generalization
Machine learning and data mining
MACHINE LEARNING DATA MINING
Focuses on prediction, based on
known properties learned from the
training data.
Focuses on the discovery of
(previously) unknown properties on
the data.
Performance is usually evaluated with
respect to the ability to reproduce
known knowledge.
The key task is the discovery of
previously unknown knowledge .
Evaluated
with respect to known knowledge
This is the algorithm part of the data
mining process
Application of algorithms to search
for patterns and
relationships that may exist in large
databases.
Algorithms
Algorithm types
Machine learning algorithms can be organized
based on the desired outcome of the algorithm or
the type of input available during training the
machine
1. Supervised learning algorithms are trained
on labeled examples, i.e., input where the
desired output is known.
2. Unsupervised learning algorithms operate on
unlabelled examples, i.e., input where the
desired output is unknown.
3. Semi-supervised learning combines both labeled
and unlabelled examples to generate an appropriate
function or classifier.
4. Reinforcement learning is concerned with
how intelligent agents ought to act in
an environment to maximize some notion of reward
from sequence of actions
Other algorithms are:
Learning to learn
Developmental learning
Transduction etc.
Algorithm types (cont’d)
Decision Tree Learning
•Decision tree learning uses a decision tree as a predictive model
which maps observations about an item to conclusions about the
item's target value.
•To classify a new instance, we start at the root and traverse the tree
to reach a leaf; at an internal node we evaluate the predicate(or
function) on the data instance, to find which child to go. The process
continues till we reach a leaf node .
•We can use greedy algorithm to build decision tree.
1. Artificial neural networks
2. Inductive logic programming
3. Support vector machines
4. Bayesian networks
5. Reinforcement learning
6. Association Rule learning
7. Clustering
Other learning techniques
Examples
•a machine learning system could be trained on email messages to learn to
distinguish between read and unread messages. After learning, it can then be
used to classify new email messages into read and unread folders.
•Optical Character Reader
Industrial chemical process control
This solution predicts the correct chemical process formulation based on past
history and current process and environmental properties.
Prediction of financial indices from textual news streams
In this application news streams from 20 high-quality sources are digitized and
used as the input to a neural network predictor of a financial time series
High explosives detector for airport security checkpoints
Oil pipeline defect recognition and quantification
Adaptive performance control of iterative linear solver
Applications of Machine Learning
Machine perception Computer vision
object recognition Natural language processing
Syntactic pattern recognition Search engines
Medical diagnosis Bioinformatics
Brain-machine interfaces Cheminformatics
Detecting credit card fraud Stock market analysis
Classifying DNA sequences Sequence mining
Speech and handwriting recognition Game playing
Software engineering Adaptive websites
Robot locomotion Computational advertising
Computational finance Structural health monitoring
opinion mining Affective computing
Information retrieval Recommender systems
Pedestrian Detection
software suites containing a variety of machine
learning algorithms.
• Ayasdi
• Apache Mahout
• Gesture Recognition Toolkit
• IBM SPSS Modeler
• MATLAB, mlpy
• Oracle Data Mining
• Orange
• Python scikit-learn
• SAS Enterprise Miner,
• STATISTICA Data Miner, and Weka
A Few Quotes
• “A breakthrough in machine learning would be worth
ten Microsofts” (Bill Gates, Chairman, Microsoft)
• “Machine learning is the next Internet”
(Tony Tether, Director, DARPA)
• Machine learning is the hot new thing”
(John Hennessy, President, Stanford)
• “Web rankings today are mostly a matter of machine learning”
(Prabhakar Raghavan, Dir. Research, Yahoo)
• “Machine learning is going to result in a real revolution”
• (Greg Papadopoulos, CTO, Sun)
• “Machine learning is today’s discontinuity”
(Jerry Yang, CEO, Yahoo)
Conclusion
Machines should be able to do all the things
what we can do & machine learning will play a
big role in achieving this goal.
References:-
1. Wikipedia
2. Machine learning summary - Greg Grudic CSCI-4830
3. CSE 546 Data Mining Machine Learning
4. Slideshare
THANK YOU !!

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Machine Learning

  • 1. Presented By:- Darshan S. Ambhaikar Sinhgad Institute of Management Pune
  • 2. TABLE OF CONTENT 1. Definition 2. What is machine learning 3. Traditional programming and machine learning 4. Why machine learning is important 5. Generalization 6. Machine learning and data mining 7. Algorithms 8. Decision tree learning 9. Other learning techniques 10.Examples 11.Applications 12.Few quotes 13.Question and answers
  • 3. Introduction Machine learning, a branch of artificial intelligence, concerns the construction and study of systems that can learn from data.
  • 4. •In 1959, Arthur Samuel defined machine learning as a "Field of study that gives computers the ability to learn without being explicitly programmed". •“The goal of machine learning is to build computer systems that can adapt and learn from their experience.” Tom Dietterich •Tom M. Mitchell provided a widely quoted, more formal definition: "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 tasks in T, as measured by P, improves with experience E" Definition Machine Learning
  • 5. So What Is Machine Learning? •Automating automation •Getting computers to program themselves •Writing software is the bottleneck Let the data do the work instead!
  • 7. Magic? No, more like gardening • Seeds • Nutrients • Gardener • Plants = Algorithms = Data = You = Programs
  • 8. Why Machine Learning is Important •Some tasks cannot be defined well, except by examples (e.g., recognizing people). •Relationships and correlations can be hidden within large amounts of data. Machine Learning/Data Mining may be able to find these relationships. •Human designers often produce machines that do not work as well as desired in the environments in which they are used.
  • 9. •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. •New knowledge about tasks is constantly being discovered by humans. It may be difficult to continuously re-design systems “by hand”. Why is Machine Learning Important (Cont’d)?
  • 10. •A core objective of a learner is to generalize from its experience. Generalization in this context is the ability of a learning machine to perform accurately on new, unseen examples/tasks after having experienced a learning data set. Generalization
  • 11. Machine learning and data mining MACHINE LEARNING DATA MINING Focuses on prediction, based on known properties learned from the training data. Focuses on the discovery of (previously) unknown properties on the data. Performance is usually evaluated with respect to the ability to reproduce known knowledge. The key task is the discovery of previously unknown knowledge . Evaluated with respect to known knowledge This is the algorithm part of the data mining process Application of algorithms to search for patterns and relationships that may exist in large databases.
  • 13. Algorithm types Machine learning algorithms can be organized based on the desired outcome of the algorithm or the type of input available during training the machine 1. Supervised learning algorithms are trained on labeled examples, i.e., input where the desired output is known. 2. Unsupervised learning algorithms operate on unlabelled examples, i.e., input where the desired output is unknown.
  • 14. 3. Semi-supervised learning combines both labeled and unlabelled examples to generate an appropriate function or classifier. 4. Reinforcement learning is concerned with how intelligent agents ought to act in an environment to maximize some notion of reward from sequence of actions Other algorithms are: Learning to learn Developmental learning Transduction etc. Algorithm types (cont’d)
  • 15. Decision Tree Learning •Decision tree learning uses a decision tree as a predictive model which maps observations about an item to conclusions about the item's target value. •To classify a new instance, we start at the root and traverse the tree to reach a leaf; at an internal node we evaluate the predicate(or function) on the data instance, to find which child to go. The process continues till we reach a leaf node . •We can use greedy algorithm to build decision tree.
  • 16. 1. Artificial neural networks 2. Inductive logic programming 3. Support vector machines 4. Bayesian networks 5. Reinforcement learning 6. Association Rule learning 7. Clustering Other learning techniques
  • 17. Examples •a machine learning system could be trained on email messages to learn to distinguish between read and unread messages. After learning, it can then be used to classify new email messages into read and unread folders. •Optical Character Reader Industrial chemical process control This solution predicts the correct chemical process formulation based on past history and current process and environmental properties. Prediction of financial indices from textual news streams In this application news streams from 20 high-quality sources are digitized and used as the input to a neural network predictor of a financial time series High explosives detector for airport security checkpoints Oil pipeline defect recognition and quantification Adaptive performance control of iterative linear solver
  • 18. Applications of Machine Learning Machine perception Computer vision object recognition Natural language processing Syntactic pattern recognition Search engines Medical diagnosis Bioinformatics Brain-machine interfaces Cheminformatics Detecting credit card fraud Stock market analysis Classifying DNA sequences Sequence mining Speech and handwriting recognition Game playing Software engineering Adaptive websites Robot locomotion Computational advertising Computational finance Structural health monitoring opinion mining Affective computing Information retrieval Recommender systems
  • 20. software suites containing a variety of machine learning algorithms. • Ayasdi • Apache Mahout • Gesture Recognition Toolkit • IBM SPSS Modeler • MATLAB, mlpy • Oracle Data Mining • Orange • Python scikit-learn • SAS Enterprise Miner, • STATISTICA Data Miner, and Weka
  • 21. A Few Quotes • “A breakthrough in machine learning would be worth ten Microsofts” (Bill Gates, Chairman, Microsoft) • “Machine learning is the next Internet” (Tony Tether, Director, DARPA) • Machine learning is the hot new thing” (John Hennessy, President, Stanford) • “Web rankings today are mostly a matter of machine learning” (Prabhakar Raghavan, Dir. Research, Yahoo) • “Machine learning is going to result in a real revolution” • (Greg Papadopoulos, CTO, Sun) • “Machine learning is today’s discontinuity” (Jerry Yang, CEO, Yahoo)
  • 22. Conclusion Machines should be able to do all the things what we can do & machine learning will play a big role in achieving this goal. References:- 1. Wikipedia 2. Machine learning summary - Greg Grudic CSCI-4830 3. CSE 546 Data Mining Machine Learning 4. Slideshare
  • 23.