CpSc 881: Machine Learning Introduction. 2 Copy Right Notice Most slides in this presentation are adopted from slides of text book and various sources.

CpSc 881: Machine Learning

Introduction

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Copy Right Notice

Most slides in this presentation are adopted from slides of text book and various sources. The Copyright belong to the original authors. Thanks!

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General Information

Class Time: 5:45 PM ~ 8:30PM Monday

Location: 119 McAdams

Instructor: Dr. Feng Luo

Office: 210 McAdams Hall

Phone: 864-656-4793

Email: [email protected]

Office Hours: 4:30PM ~ 5:30PM Monday

Web site:http://www.cs.clemson.edu/~luofeng/course/MachineLearning/

machinelearning.html

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Prerequisite

Familiarity with basic computer science principles and skills.

Familiarity with the basic mathematics, like probability theory, basic linear algebra.

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Text Book

Tom Mitchell. Machine Learning, 1997. ISBN 0-07-042807-7, WCB/McGraw-Hill

Reference books:Ethem Alpaydin. Introduction to Machine Learning, 2004. ISBN: 0-262-01211-1, the MIT Press.Nils J. Nilsson Introduction to Machine Learning, (http://robotics.stanford.edu/people/nilsson/mlbook.html)

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Grading

Grading:Mid-term exam 25 %Final exam 25 %Term project 50 %Curved to A, B, C,D

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Resources: Datasets

UCI Repository: http://www.ics.uci.edu/~mlearn/MLRepository.html

UCI KDD Archive: http://kdd.ics.uci.edu/summary.data.application.html

Statlib: http://lib.stat.cmu.edu/

Delve: http://www.cs.utoronto.ca/~delve/

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Tools

Weka (http://www.cs.waikato.ac.nz/ml/weka/)

R (http://www.r-project.org/)

Octave: A free matlab clone (http://www.gnu.org/software/octave/)

Machine Learning Tools in Java (http://sourceforge.net/projects/mldev/)

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Resources: Journals

Journal of Machine Learning Research www.jmlr.org

Machine Learning

Neural Computation

Neural Networks

IEEE Transactions on Neural Networks

IEEE Transactions on Pattern Analysis and Machine Intelligence

Annals of Statistics

Journal of the American Statistical Association

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Resources: Conferences

International Conference on Machine Learning (ICML) ICML05: http://icml.ais.fraunhofer.de/

European Conference on Machine Learning (ECML)ECML05: http://ecmlpkdd05.liacc.up.pt/

Neural Information Processing Systems (NIPS)NIPS05: http://nips.cc/

Uncertainty in Artificial Intelligence (UAI)UAI05: http://www.cs.toronto.edu/uai2005/

Computational Learning Theory (COLT)COLT05: http://learningtheory.org/colt2005/

International Joint Conference on Artificial Intelligence (IJCAI)IJCAI05: http://ijcai05.csd.abdn.ac.uk/

International Conference on Neural Networks (Europe)ICANN05: http://www.ibspan.waw.pl/ICANN-2005/

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What is Machine Learning

Definition – Computing Dictionary:

The ability of a machine to improve its performance based on previous results.

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Why Machine Learning

Human expertise does not exist (navigating on Mars)

Humans are unable to explain their expertise (speech recognition)

Solution changes in time (routing on a computer network)

Solution needs to be adapted to particular cases (user biometrics)

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Three niches for machine learning

Data Mining: using historical data to improve decisions

Medical records -> medical knowledge

Software application we can NOT program by hand

Autonomous drivingSpeech recognition

Self customizing programsNews reader that learns user interests

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Applications of Machine Learning

Information filtering/classification

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Applications of Machine Learning

Playing games

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Applications of Machine Learning

Robotics

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Applications of Machine Learning

fault detection/monitoring technical systems

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Applications of Machine Learning

bioinformatics

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Applications of Machine Learning

image classification picture processing

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Applications of Machine Learning

Text/language processing, classification, visualization, ...

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Applications of Machine Learning

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Connections of Machine Learning

MLAI

Neurobiology

ControlStatistics

OptimizationInformation theory

Psychology

Philosophy