Introduction to

Machine Learning

Odyssée Merveille and Emmanuel Roux CREATIS, Lyon

  • A short historical background

  • Supervised Learning 

  • Unsupervised Learning

  • Conclusion

Content

  A short historical background

Machine Learning

Deep Learning

Artificial Intelligence

Inspired (and simplified) from the deeplearningbook.org
(I. Goodfellow and Y. Bengio, A. Courville, 2016)

  A short historical background

Machine Learning

Deep Learning

Artificial Intelligence

Inspired from Sebastian Raschka's deep-learning course 

  A short historical background

Artificial Intelligence

Inspired from (Cardon D., Cointet J.-P., Mazieres A., 2018)

  A short historical background

Artificial Intelligence

Inspired from (Cardon D., Cointet J.-P., Mazieres A., 2018)

DEDUCTIVE

 

rule-based

no need of examples

INDUCTIVE

 

example based

adaptation

Symbolic AI

connectionism

  A short historical background

Inspired from (Cardon D., Cointet J.-P., Mazieres A., 2018)

data

outputs

Symbolic AI

program

  A short historical background

Inspired from (Cardon D., Cointet J.-P., Mazieres A., 2018)

data

expected outputs

connectionism

program

  A short historical background

Artificial Intelligence

Inspired from (Cardon D., Cointet J.-P., Mazieres A., 2018)

DEDUCTIVE

 

rule-based

no need of examples

INDUCTIVE

 

example based

adaptation

Symbolic AI

connexionism

  A short historical background

Cybernetics (40’s to 60’s)

Inspired from (Cardon D., Cointet J.-P., Mazieres A., 2018)

Symbolic AI

connexionism

Perceptron (Rosenblatt)

ADALINE (Widrow & Hoff)

Homeostat, 1948

(W. Ross Ashby)

source wikipedia

source reddit

https://isl.stanford.edu/~widrow/papers/t1960anadaptive.pdf

  A short historical background

Symbolic Artificial Intelligence (60’s to 80’s)

Inspired from (Cardon D., Cointet J.-P., Mazieres A., 2018)

Symbolic AI

connexionism

MYCIN (Shortliffe):  medical diagnoses (bacteria identification)

 GUIDON (Clancey):  teaching medical diagnostic strategy

CADUCEUS (Pople): internal medicine expert system

Transcript of an INTERNIST-I Consultation (Myers)

  A short historical background

Inspired from (Cardon D., Cointet J.-P., Mazieres A., 2018)

publication trends timeline

negative feedback

logic-based rules

backprop

McCulloch, Pitts, Hebb

Samuel, Ashby, Rosenblatt

McCarthy, Minsky, Papert, Simon,

Newell

Hinton, LeCun,

Breiman, Rumelhart

  A short historical background

Inspired from (Cardon D., Cointet J.-P., Mazieres A., 2018)

ideas trends timeline

negative feedback

logic-based rules

backprop

CIFAR-NCAP

Datasets++

GPU++

"Deepstributed"

Perceptron (Rosenblatt)

SVM (Cortes, Vapnik)

ADALINE (Widrow & Hoff)

Homoestat (Ashby)

MYCIN (Shortliffe)

 GUIDON (Clancey)

INTERNIST (Myers)

Perceptrons (Minsky, 1969)

backpropagation

(Rumelhart, )

Deep Belief Nets  (Hinton)

Macy conferences

Machine Learning

A computer program is said to learn [...] if its performance at tasks in T, as measured by a performance indicator P, improves with experience E.

reformulation of Tom Mitchel, 1978

  A short historical background

Machine learning is the field of study that gives computers the ability to learn
without being explicitly programmed.

Arthur L. Samuel, AI pionneer, 1959

A computer program is said to learn [...] if its performance at tasks in T, as measured by a performance indicator P, improves with experience E.

reformulation of Tom Mitchel, 1978

  A short historical background

Machine Learning

Machine learning is the field of study that gives computers the ability to learn
without being explicitly programmed.

Arthur L. Samuel, AI pionneer, 1959

A computer program is said to learn [...] if its performance at tasks in T, as measured by a performance indicator P, improves with experience E.

Tom Mitchel, 1978 (tweaked citation)

  A short historical background

  A short historical background

Images from PhD student
Ludmilla Penarrubia

Task

Experiment 

Performance

Learn

  A short historical background

supervised learning

Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data in Brief. 2020 Feb;28:104863. DOI: 10.1016/j.dib.2019.104863.

  A short historical background

supervised learning

Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data in Brief. 2020 Feb;28:104863. DOI: 10.1016/j.dib.2019.104863.

  A short historical background

supervised learning

Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data in Brief. 2020 Feb;28:104863. DOI: 10.1016/j.dib.2019.104863.

  A short historical background

supervised learning

Al-Dhabyani W, Gomaa M, Khaled H, Fahmy A. Dataset of breast ultrasound images. Data in Brief. 2020 Feb;28:104863. DOI: 10.1016/j.dib.2019.104863.

  A short historical background

supervised learning

unsupervised learning

  • detection (lesions)
  • classification (benign/malign)
  • segmentation (organs)
  • prediction (prognostic)
  • ...
  • detection (lesions)
  • classification (benign/malign)
  • segmentation (organs)
  • prediction (prognostic)
  • ...
  • clustering
  • dimension reduction
  • representation
  • density estimation
  • ...

  A short historical background

supervised learning

unsupervised learning

Image from PhD student
Yamil Vindas

  A short historical background

supervised learning

unsupervised learning

reinforcement

learning

self-supervised

learning

Transfert

learning

Domain Adaptation

supervised learning

unsupervised learning

semi-supervised

learning

reinforcement

learning

self-supervised

learning

Transfert

learning

Domain Adaptation

Weakly-supervised

  A short historical background

supervised learning

 supervised learning

unsupervised learning

 unsupervised learning

Conclusion

supervised learning

unsupervised learning

semi-supervised

learning

Weakly-supervised

Talk by Ismail Ben Ayed and Jose Dolz
Friday April 23
 4 pm - Paris time

Conclusion

Symbolic AI

connectionism

#responsibleAI (biases, ethics)

 

 « a priori » within learning

Hands-on session 2.x

explanable AI (xAI)

 

 

Talk by Narine Kokhlikyan

Tuesday April 20
 4.20 pm - Paris time

Conclusion

  A short historical background

supervised learning

  • The Digital Database for Screening Mammography, Michael Heath, Kevin Bowyer, Daniel Kopans, Richard Moore and W. Philip Kegelmeyer, in Proceedings of the Fifth International Workshop on Digital Mammography, M.J. Yaffe, ed., 212-218, Medical Physics Publishing, 2001. ISBN 1-930524-00-5.
  • Task
  • Experiment 
  • Performance
  • Learn

data

model

training method

loss function

  A short historical background

replace the process_fcuntion with model. loss_function... (or criterion...) etc.

source https://pytorch.org/ignite/concepts.html

Introduction to machine learning (Deep Learning for Medical Imaging School 2021)

By emmanuelrouxfr

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