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

connexionism

  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

connexionism

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

  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)

 GUIDON (Clancey)

SOPHIE (Brown)

Representation of the expression (8-6)*(3+1) as a Lisp tree

  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)

SOPHIE (Brown)

Perceptrons (Minsky, 1969)

backpropagation

(Rumelhart, )

Deep Belief Nets  (Hinton)

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

• Task

• Experiment 

• Performance

• Learn

data

model

training method

loss function

Machine Learning:

• T

• E

• P

• O

There was a loss...

... very low it could be !

If the data knows ...

... can your model see 

?

supervised learning

unsupervised learning

classification (benign/malign)

segmentation (organs)

detection (lesions)

prediction (prognostic)

clustering

dimension reduction

density estimation

supervised learning

unsupervised learning

reinforcement

learning

self-supervised

learning

Transfert

learning

Domain Adaptation

supervised learning

unsupervised learning

supervised learning

unsupervised learning

Weakly-supervised

supervised learning

unsupervised learning

Weakly-supervised

supervised learning

unsupervised learning

supervised learning