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Introduction to Applied Machine Learning for Data-Driven Science

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2018/08/30

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Introduction to Applied Machine Learning for Data-Driven Science

  1. 1. Introduction to Applied Machine Learning for Data-Driven Science Ichigaku Takigawa takigawa@ist.hokudai.ac.jp
  2. 2. Today’s talk about only 3 questions 1. What is (and what isn’t) machine learning (ML)? 2. How can we use ML for own “data-driven” science? 3. What should we take care to use ML for own problems?
  3. 3. What is (and what isn’t) machine learning? Question 1
  4. 4. Machine Learning Concerned with the question of how to construct 
 computer programs that automatically improve with experience. • Learning to recognize spoken words, handwritten characters, etc • Learning to recognize who is who by seeing faces • Learning to walk, speak, swim, ski, etc. • Learning to drive an autonomous vehicle • Learning to play world-class go, chess, shogi, etc. Tasks below would need experience rather than a single principle. Tom Mitchell
  5. 5. Machine Learning “Learning” means to automatically improve with experience “Machine” means computer programs By machine learning, let’s get computer programs to automatically improve with experience But what exactly means…. • Computer programs? 🤔 • Automatically improve with experience? 🤔
  6. 6. Computer programs Any computer programs process inputs to get outputs. Computer programsInputs Outputs
  7. 7. Design and implement computer programs Any computer programs process inputs to get outputs. Computer programsInputs Outputs • We need to explicitly know the complete procedure 
 to get outputs from inputs. 😱 • We manually code the computer program using computer
 programming languages. 😵
  8. 8. But often we don’t know how to do it 😫 Spoken words Text words • Learn to recognize spoken words, handwritten characters, etc • Learn to recognize who is who • Learn to walk, speak, swim, ski, etc. • Learn to drive an autonomous vehicle • Learn to play world-class go, chess, shogi, etc. Consider how we can construct computer programs that can Speech Recognizer
  9. 9. So we give “training data” to teach programs Speech RecognizerSpoken words Text words “hello” Inputs Outputs “machine” “learning” “automatically improve with experience (given data)” … … “training data” = examples of input-output pairs
  10. 10. (Supervised) machine learning Machine learning is a way to construct a computer program directly by a given (large) collection of input-output examples without being explicitly programmed. Generic Object Recognition Speech Recognition Machine Translation Super-Resolution Imaging AI Game Players “ ” J’aime la musique I love music
  11. 11. More typical cases with tabular data ML modelx<latexit sha1_base64="7MVBSNoyoDWzaMSxoBhJg5NShso=">AAAB+HicbVDLSgNBEOyNrxhfUY9eBoPgKeyKoMegF48JmAckS5id9CZDZnaXmVkxLvkCr3r3Jl79G69+iZNkD5pY0FBUdVNNBYng2rjul1NYW9/Y3Cpul3Z29/YPyodHLR2nimGTxSJWnYBqFDzCpuFGYCdRSGUgsB2Mb2d++wGV5nF0byYJ+pIOIx5yRo2VGo/9csWtunOQVeLlpAI56v3yd28Qs1RiZJigWnc9NzF+RpXhTOC01Es1JpSN6RC7lkZUovaz+aNTcmaVAQljZScyZK7+vsio1HoiA7spqRnpZW8m/usFcinZhNd+xqMkNRixRXCYCmJiMmuBDLhCZsTEEsoUt78TNqKKMmO7KtlSvOUKVknrouq5Va9xWand5PUU4QRO4Rw8uIIa3EEdmsAA4Rle4NV5ct6cd+djsVpw8ptj+APn8wcecJOi</latexit><latexit sha1_base64="7MVBSNoyoDWzaMSxoBhJg5NShso=">AAAB+HicbVDLSgNBEOyNrxhfUY9eBoPgKeyKoMegF48JmAckS5id9CZDZnaXmVkxLvkCr3r3Jl79G69+iZNkD5pY0FBUdVNNBYng2rjul1NYW9/Y3Cpul3Z29/YPyodHLR2nimGTxSJWnYBqFDzCpuFGYCdRSGUgsB2Mb2d++wGV5nF0byYJ+pIOIx5yRo2VGo/9csWtunOQVeLlpAI56v3yd28Qs1RiZJigWnc9NzF+RpXhTOC01Es1JpSN6RC7lkZUovaz+aNTcmaVAQljZScyZK7+vsio1HoiA7spqRnpZW8m/usFcinZhNd+xqMkNRixRXCYCmJiMmuBDLhCZsTEEsoUt78TNqKKMmO7KtlSvOUKVknrouq5Va9xWand5PUU4QRO4Rw8uIIa3EEdmsAA4Rle4NV5ct6cd+djsVpw8ptj+APn8wcecJOi</latexit><latexit sha1_base64="7MVBSNoyoDWzaMSxoBhJg5NShso=">AAAB+HicbVDLSgNBEOyNrxhfUY9eBoPgKeyKoMegF48JmAckS5id9CZDZnaXmVkxLvkCr3r3Jl79G69+iZNkD5pY0FBUdVNNBYng2rjul1NYW9/Y3Cpul3Z29/YPyodHLR2nimGTxSJWnYBqFDzCpuFGYCdRSGUgsB2Mb2d++wGV5nF0byYJ+pIOIx5yRo2VGo/9csWtunOQVeLlpAI56v3yd28Qs1RiZJigWnc9NzF+RpXhTOC01Es1JpSN6RC7lkZUovaz+aNTcmaVAQljZScyZK7+vsio1HoiA7spqRnpZW8m/usFcinZhNd+xqMkNRixRXCYCmJiMmuBDLhCZsTEEsoUt78TNqKKMmO7KtlSvOUKVknrouq5Va9xWand5PUU4QRO4Rw8uIIa3EEdmsAA4Rle4NV5ct6cd+djsVpw8ptj+APn8wcecJOi</latexit><latexit sha1_base64="7MVBSNoyoDWzaMSxoBhJg5NShso=">AAAB+HicbVDLSgNBEOyNrxhfUY9eBoPgKeyKoMegF48JmAckS5id9CZDZnaXmVkxLvkCr3r3Jl79G69+iZNkD5pY0FBUdVNNBYng2rjul1NYW9/Y3Cpul3Z29/YPyodHLR2nimGTxSJWnYBqFDzCpuFGYCdRSGUgsB2Mb2d++wGV5nF0byYJ+pIOIx5yRo2VGo/9csWtunOQVeLlpAI56v3yd28Qs1RiZJigWnc9NzF+RpXhTOC01Es1JpSN6RC7lkZUovaz+aNTcmaVAQljZScyZK7+vsio1HoiA7spqRnpZW8m/usFcinZhNd+xqMkNRixRXCYCmJiMmuBDLhCZsTEEsoUt78TNqKKMmO7KtlSvOUKVknrouq5Va9xWand5PUU4QRO4Rw8uIIa3EEdmsAA4Rle4NV5ct6cd+djsVpw8ptj+APn8wcecJOi</latexit> y<latexit sha1_base64="IAkI/WnulkwRo6OBvH4htvdy0fo=">AAAB+HicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLoxmUL9gFtKJPpTTt0JgkzEyGGfoFb3bsTt/6NW7/EaZuFVg9cOJxzL+dygkRwbVz30ymtrW9sbpW3Kzu7e/sH1cOjjo5TxbDNYhGrXkA1Ch5h23AjsJcopDIQ2A2mt3O/+4BK8zi6N1mCvqTjiIecUWOlVjas1ty6uwD5S7yC1KBAc1j9GoxilkqMDBNU677nJsbPqTKcCZxVBqnGhLIpHWPf0ohK1H6+eHRGzqwyImGs7ESGLNSfFzmVWmcysJuSmole9ebiv14gV5JNeO3nPEpSgxFbBoepICYm8xbIiCtkRmSWUKa4/Z2wCVWUGdtVxZbirVbwl3Qu6p5b91qXtcZNUU8ZTuAUzsGDK2jAHTShDQwQnuAZXpxH59V5c96XqyWnuDmGX3A+vgEgBJOj</latexit><latexit sha1_base64="IAkI/WnulkwRo6OBvH4htvdy0fo=">AAAB+HicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLoxmUL9gFtKJPpTTt0JgkzEyGGfoFb3bsTt/6NW7/EaZuFVg9cOJxzL+dygkRwbVz30ymtrW9sbpW3Kzu7e/sH1cOjjo5TxbDNYhGrXkA1Ch5h23AjsJcopDIQ2A2mt3O/+4BK8zi6N1mCvqTjiIecUWOlVjas1ty6uwD5S7yC1KBAc1j9GoxilkqMDBNU677nJsbPqTKcCZxVBqnGhLIpHWPf0ohK1H6+eHRGzqwyImGs7ESGLNSfFzmVWmcysJuSmole9ebiv14gV5JNeO3nPEpSgxFbBoepICYm8xbIiCtkRmSWUKa4/Z2wCVWUGdtVxZbirVbwl3Qu6p5b91qXtcZNUU8ZTuAUzsGDK2jAHTShDQwQnuAZXpxH59V5c96XqyWnuDmGX3A+vgEgBJOj</latexit><latexit sha1_base64="IAkI/WnulkwRo6OBvH4htvdy0fo=">AAAB+HicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLoxmUL9gFtKJPpTTt0JgkzEyGGfoFb3bsTt/6NW7/EaZuFVg9cOJxzL+dygkRwbVz30ymtrW9sbpW3Kzu7e/sH1cOjjo5TxbDNYhGrXkA1Ch5h23AjsJcopDIQ2A2mt3O/+4BK8zi6N1mCvqTjiIecUWOlVjas1ty6uwD5S7yC1KBAc1j9GoxilkqMDBNU677nJsbPqTKcCZxVBqnGhLIpHWPf0ohK1H6+eHRGzqwyImGs7ESGLNSfFzmVWmcysJuSmole9ebiv14gV5JNeO3nPEpSgxFbBoepICYm8xbIiCtkRmSWUKa4/Z2wCVWUGdtVxZbirVbwl3Qu6p5b91qXtcZNUU8ZTuAUzsGDK2jAHTShDQwQnuAZXpxH59V5c96XqyWnuDmGX3A+vgEgBJOj</latexit><latexit sha1_base64="IAkI/WnulkwRo6OBvH4htvdy0fo=">AAAB+HicbVDLSsNAFL2pr1pfVZduBovgqiQi6LLoxmUL9gFtKJPpTTt0JgkzEyGGfoFb3bsTt/6NW7/EaZuFVg9cOJxzL+dygkRwbVz30ymtrW9sbpW3Kzu7e/sH1cOjjo5TxbDNYhGrXkA1Ch5h23AjsJcopDIQ2A2mt3O/+4BK8zi6N1mCvqTjiIecUWOlVjas1ty6uwD5S7yC1KBAc1j9GoxilkqMDBNU677nJsbPqTKcCZxVBqnGhLIpHWPf0ohK1H6+eHRGzqwyImGs7ESGLNSfFzmVWmcysJuSmole9ebiv14gV5JNeO3nPEpSgxFbBoepICYm8xbIiCtkRmSWUKa4/Z2wCVWUGdtVxZbirVbwl3Qu6p5b91qXtcZNUU8ZTuAUzsGDK2jAHTShDQwQnuAZXpxH59V5c96XqyWnuDmGX3A+vgEgBJOj</latexit> Inputs Outputs
  12. 12. Underlying principle: Use of statistical trends We see common patterns (empirical rules) emerge from observing many examples, which we cannot recognize when we see only a few. Characterize the difference between 
 not by explicit rules, but by implicit statistical rules directly defined by many observations. “ ” and “ ”
  13. 13. Patterns emerge from many examples? Get weight(g) and height(cm)
  14. 14. Patterns emerge from many examples? Get weight(g) and height(cm) 5 6.25 7.5 8.75 10 90112.5135157.5180 ● ● ●● ● ● ● ● ●● weight(g) height(cm) Apple Orange
  15. 15. Patterns emerge from many examples? Get weight(g) and height(cm) 5 6.25 7.5 8.75 10 90112.5135157.5180 ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● weight(g) height(cm) Apple Orange
  16. 16. Patterns emerge from many examples? Get weight(g) and height(cm) 5 6.25 7.5 8.75 10 90112.5135157.5180 ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● weight(g) height(cm) Apple Orange
  17. 17. Patterns emerge from many examples? Get weight(g) and height(cm) 5 6.25 7.5 8.75 10 90112.5135157.5180 ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● weight(g) height(cm) Apple Orange
  18. 18. Patterns emerge from many examples? Get weight(g) and height(cm) 5 6.25 7.5 8.75 10 90112.5135157.5180 ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● weight(g) height(cm) Apple Orange
  19. 19. Patterns emerge from many examples? Get weight(g) and height(cm) 5 6.25 7.5 8.75 10 90112.5135157.5180 ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● weight(g) height(cm) Apple Orange
  20. 20. Patterns emerge from many examples? Get weight(g) and height(cm) 5 6.25 7.5 8.75 10 90112.5135157.5180 ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● weight(g) height(cm) Apple Orange
  21. 21. Patterns emerge from many examples? Get weight(g) and height(cm) 5 6.25 7.5 8.75 10 90112.5135157.5180 ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ●●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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  22. 22. Patterns emerge from many examples? Get weight(g) and height(cm) 5 6.25 7.5 8.75 10 90112.5135157.5180 ● ● ●● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ●● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 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  23. 23. This is all about (supervised) machine learning Get weight(g) and height(cm) Fruit SorterInputs Outputs Now we got a computer program for this classification problem. weight(g) height(cm) “features” or “descriptors” Apple or Orange “class label” or “response”

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