Transcript Machine Learning

Artificial Intelligence and Applications
School of Computer Science and Engineering
Bai Xiao
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Xiao Bai(Family name)
Associate Professor，Ph.D. Supervisor
B.S., Computer Science, Beihang University
(BUAA)
Ph. D. Computer Science, University of York,
U.K.
Research Interests: Computer Vision, Pattern
Recognition, Image Processing, Remote
Sensing Image Analysis, Machine Learning
公共邮箱:
[email protected]
FTP:
ftp://219.224.169.249/
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Artificial Intelligence is a broad area focus on
how to let machines to have intelligence.
It is also a modern research area.
We will mainly learn machine learning
especially learning algorithms, applications.
Machine learning, pattern recognition, vision
based learning and the corresponding
applications.
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Research based courses, not exam based.
Teaching, Assignment(30%),
Attendance(30%), Final report(40%).
Assignment: Reading research documents, i.e.
research papers, books; presentation; coding
assignment; applications and etc.
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Books:
• R. Duda, P. Hart & D. Stork, Pattern
Classification (2nd ed.), Wiley (Required)
• T. Mitchell, Machine Learning,
McGraw-Hill (Recommended)
• Papers: Canonical Papers and Recent Papers
on this area.
• Reports: From Experts or Researcher about
their recent research progress.
Introduction to Machine Learning
Bai Xiao
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)
What is “Machine Learning”
• Machine learning is a subfield of computer science that evolved
from the study of pattern recognition and computational
learning theory in artificial intelligence.
• Machine learning explores the study and construction of
algorithms that can learn from and make predictions on data.
• Such algorithms operate by building a model from example
inputs in order to make data-driven predictions or decisions,
rather than following strictly static program instructions.
Traditional Programming
Data
Program
Computer
Output
Machine Learning
Data
Output
Computer
Program
Sample Applications
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Web search
Computational biology
Finance
E-commerce
Space exploration
Robotics
Information extraction
Social networks
Debugging
[Your favorite area]
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Machine Learning Algorithms: Decision Tree,
Concept Learning, Neural Network, Support
Vector Machine, Clustering, Bayesian
Learning, Reinforcement Learning,
Dictionary based Learning, Semi-supervised
Learning
Application: Image Classification, Image
Recognition, Image Retrieval.
Interesting Topics: some related ongoing
research topics.
ML in a Nutshell
• Tens of thousands of machine learning
algorithms
• Hundreds new every year
• Every machine learning algorithm has three
components:
– Representation
– Evaluation
– Optimization
Representation
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Decision trees
Sets of rules / Logic programs
Instances
Graphical models (Bayes/Markov nets)
Neural networks
Support vector machines
Model ensembles
Etc.
Evaluation
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Accuracy
Precision and recall
Squared error
Likelihood
Posterior probability
Cost / Utility
Margin
Entropy
K-L divergence
Etc.
Optimization
• Combinatorial optimization
– E.g.: Greedy search
• Convex optimization
– E.g.: Gradient descent
• Constrained optimization
– E.g.: Linear programming