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The presentations

 Data Science Summer School presentations

To download the presentations (slides)

 

 Monday 28/08  Tuesday 29/08  Wednesday 30/08  Thursday 30/08  Friday 1/09

Yoshua Bengio

Deep learning for AI

Yoshua Bengio

Deep feedforward neural
networks  part 2

Pradeep Ravikumar

Graphical models :
Representations. Part 3

Peter Richtárik


Randomized optimization
methods. Part 4

Csaba Szepesvári


Stochastic, finite-armed
bandits. Part 3

Sean Meyn

Hidden theory and
new super-fast algorithms

Pradeep Ravikumar

Graphical models :
Representations. Part 1

Pradeep Ravikumar


Graphical models :
Learning

Peter Richtárik


Randomized optimization
methods. Part 5

Csaba Szepesvári


Adversarial Bandits
 Part 1

Yoshua Bengio

Computational flow graph
and back-propagation

Yoshua Bengio


Deep nets : Tricks and
tips

Pradeep Ravikumar

Graphical models :
Inference

Stuart Russell


Open-universe probability
models.

Sebastian Nowozin


Unsupervised learning and
representation learning

Yoshua Bengio

Deep feedforward neural
networks part 1

Pradeep Ravikumar


Graphical models :
Representations. Part 2

Peter Richtárik


Randomized optimization
methods. Part 1

Csaba Szepesvári


Stochastic, finite-armed
bandits. Part 1

Cédric Archambeau


Bayesian optimization
 

Damien Ernst

Future electric power
energy transition

 

Peter Richtárik


Randomized optimization
methods. Part 2

Csaba Szepesvári


Stochastic, finite-armed
bandits. Part 2

Cédric Archambeau


Amazon : a playground for
machine learning

Sean Meyn


Distributed control and
markets.

 

Peter Richtárik


Randomized optimization
methods. Part 3

Olivier Bousquet


Why deep learning works?

Laura Grigori


Communication avoiding
algorithms

Round table discussion


RT: Smarts grids

   

Olivier Bousquet


Machine learning meets
the real world

Csaba Szepesvári


Adversarial Bandits
 Part 2

     

Round table discussion

Table ronde : Future of AI
RT: Future of AI

Csaba Szepesvári


Stochastic linear bandits