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기계학습(Machine learning) 입문하기

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기계학습의 전반적인 개념에 대한 입문 강의입니다. 강의 영상은 다음에서 확인하실 수 있습니다.
(http://t-robotics.blogspot.com)
(http://terryum.io)

Published in: Engineering
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기계학습(Machine learning) 입문하기

  1. 1. Terry Taewoong Um (terry.t.um@gmail.com) University of Waterloo Department of Electrical & Computer Engineering Terry Taewoong Um INTRODUCTION TO MACHINE LEARNING AND DEEP LEARNING 1 T-robotics.blogspot.com Facebook.com/TRobotics
  2. 2. Terry Taewoong Um (terry.t.um@gmail.com) CAUTION • I cannot explain everything • You cannot get every details 2 • Try to get a big picture • Get some useful keywords • Connect with your research
  3. 3. Terry Taewoong Um (terry.t.um@gmail.com) CONTENTS 1. What is Machine Learning? 2. What is Deep Learning? 3
  4. 4. Terry Taewoong Um (terry.t.um@gmail.com) CONTENTS 4 1. What is Machine Learning?
  5. 5. Terry Taewoong Um (terry.t.um@gmail.com) 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 tactics 5 T : Win the game P : Goals E : (x) Players’ movements (y) Evaluation
  6. 6. Terry Taewoong Um (terry.t.um@gmail.com) 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)
  7. 7. Terry Taewoong Um (terry.t.um@gmail.com) TASKS 7 classification discrete target values x : pixels (28*28) y : 0,1, 2,3,…,9 regression real target values x ∈ (0,100) y : 0,1, 2,3,…,9 clustering no target values x ∈ (-3,3)×(-3,3) "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)
  8. 8. Terry Taewoong Um (terry.t.um@gmail.com) PERFORMANCE 8 "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
  9. 9. Terry Taewoong Um (terry.t.um@gmail.com) EXPERIENCE 9 "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)
  10. 10. Terry Taewoong Um (terry.t.um@gmail.com) A TOY EXAMPLE 10 ? Height(cm) Weight (kg) [Input X] [Output Y]
  11. 11. Terry Taewoong Um (terry.t.um@gmail.com) 11 180 Height(cm) Weight (kg) 80 Y = aX+b Model : Y = aX+b Parameter : (a, b) [Goal] Find (a,b) which best fits the given data A TOY EXAMPLE
  12. 12. Terry Taewoong Um (terry.t.um@gmail.com) 12 [Analytic Solution] Least square problem (from AX = b, X=A#b where A# is A’s 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 problem http://mnemstudio.org/neural-networks- multilayer-perceptron-design.htm A TOY EXAMPLE
  13. 13. Terry Taewoong Um (terry.t.um@gmail.com) 13 32 Age(year) Running Record (min) 140 WHAT WOULD BE THE CORRECT MODEL? Select a model → Set a cost function → Optimization
  14. 14. Terry Taewoong Um (terry.t.um@gmail.com) 14 ? X Y WHAT WOULD BE THE CORRECT MODEL? 1. Regularization 2. Nonparametric model “overfitting”
  15. 15. Terry Taewoong Um (terry.t.um@gmail.com) 15 L2 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
  16. 16. Terry Taewoong Um (terry.t.um@gmail.com) 16 WHAT WOULD BE THE CORRECT MODEL? 1. Regularization 2. Nonparametric model training time error training error test error we should stop here training set validation set test set for training (parameter optimization) for early stopping (avoid overfitting) for evaluation (measure the performance) keep watching the validation error
  17. 17. Terry Taewoong Um (terry.t.um@gmail.com) 17 NONPARAMETRIC MODEL • It 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 data • Gaussian Process Regression (GPR), a sort of kernel method, is a widely-used nonparametric regression method • Support Vector Machine (SVM), also a sort of kernel method, is a widely-used nonparametric classification method kernel function [Input space] [Feature space]
  18. 18. Terry Taewoong Um (terry.t.um@gmail.com) 18 SUPPORT 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 function kernel function
  19. 19. Terry Taewoong Um (terry.t.um@gmail.com) 19 GAUSSIAN PROCESS REGRESSION (GPR) https://youtu.be/YqhLnCm0KXY https://youtu.be/kvPmArtVoFE • Gaussian Distribution • Multivariate regression likelihood posterior prior likelihood prediction conditioning the joint distribution of the observed & predicted values https://goo.gl/EO54WN http://goo.gl/XvOOmf
  20. 20. Terry Taewoong Um (terry.t.um@gmail.com) 20 DIMENSION REDUCTION [Original space] [Feature space] low dim. high dim. high dim. low dim. 𝑋 → ∅(𝑋) • Principal Component Analysis : Find the best orthogonal axes (=principal components) which maximize the variance of the data Y = P X * The rows in P are m largest eigenvectors of 1 𝑁 𝑋𝑋 𝑇 (covariance matrix)
  21. 21. Terry Taewoong Um (terry.t.um@gmail.com) 21 DIMENSION REDUCTION http://jbhuang0604.blogspot.kr/2013/04/miss-korea-2013-contestants-face.html
  22. 22. Terry Taewoong Um (terry.t.um@gmail.com) 22 SUMMARY - PART 1 • Machine 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 set • Nonparametric model : Support Vector Machine, Gaussian Process Regression • Dimension reduction : used as pre-processing data

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