기계학습(Machine learning) 입문하기

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Terry Taewoong Um ([email protected]) University of Waterloo Department of Electrical & Computer Engineering

Terry Taewoong Um

Introduction to Machine Learning and Deep Learning1T-robotics.blogspot.comFacebook.com/TRobotics

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Terry Taewoong Um ([email protected])CAUTIONI cannot explain everythingYou cannot get every details 2

Try to get a big pictureGet some useful keywordsConnect with your research

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Terry Taewoong Um ([email protected])ContentsWhat is Machine Learning?

What is Deep Learning?3

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Terry Taewoong Um ([email protected])Contents4What is Machine Learning?

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Terry Taewoong Um ([email protected])What is Machine Learning?"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 T. Michell (1997)

Example: A program for soccer tactics5T : Win the gameP : GoalsE : (x) Players movements (y) Evaluation

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Terry Taewoong Um ([email protected])What is Machine Learning?6

Toward learning robot table tennis, J. Peters et al. (2012) https://youtu.be/SH3bADiB7uQ"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 T. Michell (1997)

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Terry Taewoong Um ([email protected])Tasks7 classification discrete target values

x : pixels (28*28)y : 0,1, 2,3,,9

regression real target valuesy : 0,1, 2,3,,9

clustering no target values"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 T. Michell (1997)

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Terry Taewoong Um ([email protected])Performance8"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 T. Michell (1997)

classification 0-1 loss function

regression L2 loss function

clustering

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Terry Taewoong Um ([email protected])eXperience9"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 T. Michell (1997)

classification labeled data

(pixels)(number)

regression labeled data

(x) (y)

clustering

unlabeled data

(x1,x2)

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Terry Taewoong Um ([email protected])A Toy Example10

?Height(cm)Weight(kg)[Input X][Output Y]

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180Height(cm)Weight(kg)

80Y = aX+bModel : Y = aX+bParameter : (a, b)

[Goal] Find (a,b) which best fits the given dataA Toy Example

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Terry Taewoong Um ([email protected])12[Analytic Solution] Least square problem

(from AX = b, X=A#b whereA# is As pseudo inverse)Not always available

[Numerical Solution]1. Set a cost function

2. Apply an optimization method (e.g. Gradient Descent (GD) Method)

L(a,b)http://www.yaldex.com/game-development/1592730043_ch18lev1sec4.html

Local minima problemhttp://mnemstudio.org/neural-networks-multilayer-perceptron-design.htmA Toy Example

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32Age(year)RunningRecord(min)140

What would be the correct model?Select a model Set a cost function Optimization

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?XY

What would be the correct model?1. Regularization2. Nonparametric modeloverfitting

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Terry Taewoong Um ([email protected])15L2 Regularization

(e.g. w=(a,b) where Y=aX+b)

Avoid a complicated model!

Another interpretation : : Maximum a Posteriori (MAP)http://goo.gl/6GE2ix

http://goo.gl/6GE2ix

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Terry Taewoong Um ([email protected])16What would be the correct model?1. Regularization2. Nonparametric model

training timeerror

training errortest errorwe should stop here

trainingsetvalidationsettestsetfor training(parameter optimization)for early stopping(avoid overfitting)for evaluation(measure theperformance)

keep watching the validation error

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Terry Taewoong Um ([email protected])17NonParametric ModelIt does not assume any parametric models (e.g. Y = aX+b, Y=aX2+bX+c, etc.)It often requires much more samples

Kernel methods are frequently applied for modeling the dataGaussian Process Regression (GPR), a sort of kernel method, is a widely-used nonparametric regression methodSupport Vector Machine (SVM), also a sort of kernel method, is a widely-used nonparametric classification method

kernel function[Input space][Feature space]

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Terry Taewoong Um ([email protected])18Support Vector Machine (SVM)

Myo, Thalmic Labs (2013) https://youtu.be/oWu9TFJjHaM

[Linear classifiers][Maximum margin]

Support vector Machine Tutorial, J. Weston, http://goo.gl/19ywcj

[Dual formulation] ( )kernel functionkernel function

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Terry Taewoong Um ([email protected])19Gaussian Process Regression (GPR)https://youtu.be/YqhLnCm0KXY

https://youtu.be/kvPmArtVoFE

Gaussian DistributionMultivariate regression likelihoodposteriorpriorlikelihood

predictionconditioning the joint distribution of the observed & predicted values

https://goo.gl/EO54WN

http://goo.gl/XvOOmf

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Terry Taewoong Um ([email protected])20Dimension reduction

[Original space][Feature space]

low dim.high dim.high dim.low dim.Principal Component Analysis

: Find the best orthogonal axes (=principal components) which maximize the variance of the data

Y = P X

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Terry Taewoong Um ([email protected])21Dimension reduction

http://jbhuang0604.blogspot.kr/2013/04/miss-korea-2013-contestants-face.html

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Terry Taewoong Um ([email protected])22SUMMARY - Part 1Machine Learning - Tasks : Classification, Regression, Clustering, etc. - Performance : 0-1 loss, L2 loss, etc. - Experience : labeled data, unlabelled data Machine Learning Process (1) Select a parametric / nonparametric model (2) Set a performance measurement including regularization term (3) Training data (optimizing parameters) until validation error increases (4) Evaluate the final performance using test setNonparametric model : Support Vector Machine, Gaussian Process RegressionDimension reduction : used as pre-processing data

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기계학습(Machine learning) 입문하기

Engineering

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