Cours 3 : Groupes abéliens, groupe symétrique.

[\newcommand\yoneda{ {\bf y}} \newcommand\oppositeName{ {\rm op}} \newcommand\opposite[1]{ {#1}^\oppositeName} \newcommand\id[1][{}]{ {\rm id}{#1}} \newcomma...

Lecture 1: Basics

[\newcommand\yoneda{ {\bf y}} \newcommand\oppositeName{ {\rm op}} \newcommand\opposite[1]{ {#1}^\oppositeName} \newcommand\id[1][{}]{ {\rm id}{#1}} \newcomma...

Lecture 1: Basics

[\newcommand\yoneda{ {\bf y}} \newcommand\oppositeName{ {\rm op}} \newcommand\opposite[1]{ {#1}^\oppositeName} \newcommand\id[1][{}]{ {\rm id}{#1}} \newcomma...

Sydney Reading Group: Classifying Category

[\newcommand\yoneda{ {\bf y}} \newcommand\oppositeName{ {\rm op}} \newcommand\opposite[1]{ {#1}^\oppositeName} \newcommand\id[1][{}]{ {\rm id}{#1}} \newcomma...

Lecture 1: Basics

[\newcommand\yoneda{ {\bf y}} \newcommand\oppositeName{ {\rm op}} \newcommand\opposite[1]{ {#1}^\oppositeName} \newcommand\id[1][{}]{ {\rm id}{#1}} \newcomma...

Lecture 1: Basics

[\newcommand\yoneda{ {\bf y}} \newcommand\oppositeName{ {\rm op}} \newcommand\opposite[1]{ {#1}^\oppositeName} \newcommand\id[1][{}]{ {\rm id}{#1}} \newcomma...

Sydney Reading Group: Classifying Category

[\newcommand\yoneda{ {\bf y}} \newcommand\oppositeName{ {\rm op}} \newcommand\opposite[1]{ {#1}^\oppositeName} \newcommand\id[1][{}]{ {\rm id}{#1}} \newcomma...

Sydney Reading Group: Classifying Category

[\newcommand\yoneda{ {\bf y}} \newcommand\oppositeName{ {\rm op}} \newcommand\opposite[1]{ {#1}^\oppositeName} \newcommand\id[1][{}]{ {\rm id}{#1}} \newcomma...

Sydney Reading Group: Classifying Category

[\newcommand\yoneda{ {\bf y}} \newcommand\oppositeName{ {\rm op}} \newcommand\opposite[1]{ {#1}^\oppositeName} \newcommand\id[1][{}]{ {\rm id}{#1}} \newcomma...

Lecture 1: Basics

[\newcommand\yoneda{ {\bf y}} \newcommand\oppositeName{ {\rm op}} \newcommand\opposite[1]{ {#1}^\oppositeName} \newcommand\id[1][{}]{ {\rm id}{#1}} \newcomma...

Sydney Reading Group: Classifying Category

[\newcommand\yoneda{ {\bf y}} \newcommand\oppositeName{ {\rm op}} \newcommand\opposite[1]{ {#1}^\oppositeName} \newcommand\id[1][{}]{ {\rm id}{#1}} \newcomma...

Exercises 10: BPP, AM protocols, AM hierarchy

EX0: BPP-completeness

Exercises 11: BPP, ABPP, PP

Proofs to bear in mind

Exercises 12: Review

NP, coNP

Exercises 10: BPP, AM protocols, AM hierarchy

EX0: BPP-completeness

Exercises 11: BPP, ABPP, PP

Proofs to bear in mind

Exercises 12: Review

NP, coNP

DM: Advanced Complexity

Homework Assignment : Advanced complexity.

DM: Advanced Complexity

Homework Assignment : Advanced complexity.

Lecture 7: RP, coRP, ZPP, BPP

RP, coRP, ZPP, BPP

Lecture 8: BPP, P/Poly

Karp-Lipton

Lecture 9: The PCP theorem

cf. CSE 533: The PCP Theorem and Hardness of approximation (autumn 2005)

Exercises 1 : SPACE and NL

EX 1: Graph representation and why it doesn’t matter

Exercises 2 : SPACE and NL (2)

EX 1 : Warm up

Exercises 3: Space hierarchy theorem

EX 1: Space hierarchy theorem

Exercises 4: PSPACE-complete problems, Padding

EX 1: Language theory

Exercises 5: Prime Numbers, P-complete problems

EX 1: Unary languages

DM: Advanced Complexity

Homework Assignment : Advanced complexity.

Exercises 12: Review

NP, coNP

Exercises 10: BPP, AM protocols, AM hierarchy

EX0: BPP-completeness

Exercises 11: BPP, ABPP, PP

Proofs to bear in mind

Exercises 12: Review

NP, coNP

DM: Advanced Complexity

Homework Assignment : Advanced complexity.

Lecture 1 : Speedup theorem

Outline

Lecture 2: Logspace

Webpage of the course

Lecture 3:

Immermann-Szelepcsenyi (1987)

Lecture 4: Alternating Turing Machines

Recap:

Lecture 5: P=AL

Last week:

Lecture 6: Polynomial-time hierarchy

A visit to the polynomial-time hierarchy

Lecture 1 : Speedup theorem

Outline

Exercises 1 : SPACE and NL

EX 1: Graph representation and why it doesn’t matter

Lecture 2: Logspace

Webpage of the course

Exercises 2 : SPACE and NL (2)

EX 1 : Warm up

Lecture 3:

Immermann-Szelepcsenyi (1987)

Exercises 3: Space hierarchy theorem

EX 1: Space hierarchy theorem

Lecture 4: Alternating Turing Machines

Recap:

Exercises 4: PSPACE-complete problems, Padding

EX 1: Language theory

Lecture 5: P=AL

Last week:

Exercises 5: Prime Numbers, P-complete problems

EX 1: Unary languages

Lecture 6: Polynomial-time hierarchy

A visit to the polynomial-time hierarchy

Lecture 7: RP, coRP, ZPP, BPP

RP, coRP, ZPP, BPP

Exercises 10: BPP, AM protocols, AM hierarchy

EX0: BPP-completeness

Lecture 8: BPP, P/Poly

Karp-Lipton

Exercises 11: BPP, ABPP, PP

Proofs to bear in mind

Lecture 9: The PCP theorem

cf. CSE 533: The PCP Theorem and Hardness of approximation (autumn 2005)

Exercises 12: Review

NP, coNP

DM: Advanced Complexity

Homework Assignment : Advanced complexity.

Lecture 1 : Speedup theorem

Outline

Exercises 1 : SPACE and NL

EX 1: Graph representation and why it doesn’t matter

Lecture 2: Logspace

Webpage of the course

Exercises 2 : SPACE and NL (2)

EX 1 : Warm up

Lecture 3:

Immermann-Szelepcsenyi (1987)

Exercises 3: Space hierarchy theorem

EX 1: Space hierarchy theorem

Lecture 4: Alternating Turing Machines

Recap:

Exercises 4: PSPACE-complete problems, Padding

EX 1: Language theory

Lecture 5: P=AL

Last week:

Exercises 5: Prime Numbers, P-complete problems

EX 1: Unary languages

Lecture 6: Polynomial-time hierarchy

A visit to the polynomial-time hierarchy

Lecture 7: RP, coRP, ZPP, BPP

RP, coRP, ZPP, BPP

Exercises 10: BPP, AM protocols, AM hierarchy

EX0: BPP-completeness

Lecture 8: BPP, P/Poly

Karp-Lipton

Exercises 11: BPP, ABPP, PP

Proofs to bear in mind

Lecture 9: The PCP theorem

cf. CSE 533: The PCP Theorem and Hardness of approximation (autumn 2005)

Exercises 12: Review

NP, coNP

Lecture 1 : Speedup theorem

Outline

Exercises 1 : SPACE and NL

EX 1: Graph representation and why it doesn’t matter

Lecture 2: Logspace

Webpage of the course

Exercises 2 : SPACE and NL (2)

EX 1 : Warm up

Lecture 3:

Immermann-Szelepcsenyi (1987)

Exercises 3: Space hierarchy theorem

EX 1: Space hierarchy theorem

Lecture 4: Alternating Turing Machines

Recap:

Exercises 4: PSPACE-complete problems, Padding

EX 1: Language theory

Lecture 5: P=AL

Last week:

Exercises 5: Prime Numbers, P-complete problems

EX 1: Unary languages

Lecture 6: Polynomial-time hierarchy

A visit to the polynomial-time hierarchy

Lecture 7: RP, coRP, ZPP, BPP

RP, coRP, ZPP, BPP

Exercises 10: BPP, AM protocols, AM hierarchy

EX0: BPP-completeness

Lecture 8: BPP, P/Poly

Karp-Lipton

Exercises 11: BPP, ABPP, PP

Proofs to bear in mind

Lecture 9: The PCP theorem

cf. CSE 533: The PCP Theorem and Hardness of approximation (autumn 2005)

Exercises 12: Review

NP, coNP

Exercises 11: BPP, ABPP, PP

Proofs to bear in mind

Exercises 12: Review

NP, coNP

Exercises 1 : SPACE and NL

EX 1: Graph representation and why it doesn’t matter

Exercises 2 : SPACE and NL (2)

EX 1 : Warm up

Exercises 3: Space hierarchy theorem

EX 1: Space hierarchy theorem

Exercises 4: PSPACE-complete problems, Padding

EX 1: Language theory

Exercises 5: Prime Numbers, P-complete problems

EX 1: Unary languages

Exercises 10: BPP, AM protocols, AM hierarchy

EX0: BPP-completeness

DM: Advanced Complexity

Homework Assignment : Advanced complexity.

DM: Advanced Complexity

Homework Assignment : Advanced complexity.

Lecture 1 : Speedup theorem

Outline

Exercises 1 : SPACE and NL

EX 1: Graph representation and why it doesn’t matter

Lecture 2: Logspace

Webpage of the course

Exercises 2 : SPACE and NL (2)

EX 1 : Warm up

Lecture 3:

Immermann-Szelepcsenyi (1987)

Exercises 3: Space hierarchy theorem

EX 1: Space hierarchy theorem

Lecture 4: Alternating Turing Machines

Recap:

Exercises 4: PSPACE-complete problems, Padding

EX 1: Language theory

Lecture 5: P=AL

Last week:

Exercises 5: Prime Numbers, P-complete problems

EX 1: Unary languages

Lecture 6: Polynomial-time hierarchy

A visit to the polynomial-time hierarchy

Lecture 7: RP, coRP, ZPP, BPP

RP, coRP, ZPP, BPP

Lecture 8: BPP, P/Poly

Karp-Lipton

Lecture 9: The PCP theorem

cf. CSE 533: The PCP Theorem and Hardness of approximation (autumn 2005)

Lecture 3:

Immermann-Szelepcsenyi (1987)

Lecture 4: Alternating Turing Machines

Recap:

Lecture 5: P=AL

Last week:

Lecture 6: Polynomial-time hierarchy

A visit to the polynomial-time hierarchy

Lecture 7: RP, coRP, ZPP, BPP

RP, coRP, ZPP, BPP

Lecture 8: BPP, P/Poly

Karp-Lipton

Lecture 9: The PCP theorem

cf. CSE 533: The PCP Theorem and Hardness of approximation (autumn 2005)

Lecture 1 : Speedup theorem

Outline

Lecture 2: Logspace

Webpage of the course

DM: Advanced Complexity

Homework Assignment : Advanced complexity.

Computational Geometry Learning: Introduction

Teacher: Marc Glisse

Homology theory

Teacher: Clément Maria

Discrete Morse theory

Teacher: Clément Maria

Persistent Homology

Cech complex

Compatible basis, Stability of persistent homology, and Homology inference

[K_0 = ∅ \overset{σ_1}{\hookrightarrow} K_1 \overset{σ_2}{\hookrightarrow} K_2 \overset{σ_3}{\hookrightarrow} ⋯ \overset{σ_n}{\hookrightarrow} K_n = \lbrace ...

Computational Geometry Learning: Introduction

Teacher: Marc Glisse

Homology theory

Teacher: Clément Maria

Discrete Morse theory

Teacher: Clément Maria

Persistent Homology

Cech complex

Compatible basis, Stability of persistent homology, and Homology inference

[K_0 = ∅ \overset{σ_1}{\hookrightarrow} K_1 \overset{σ_2}{\hookrightarrow} K_2 \overset{σ_3}{\hookrightarrow} ⋯ \overset{σ_n}{\hookrightarrow} K_n = \lbrace ...

Exercise Sheet 4: Covariance and Correlation, Bayes’ theorem, and Linear discriminant analysis

Exercise 1: softmax Gibbs-policy

Exercise Sheet 2: Temporal-Difference learning with discounting

Exercise Sheet 2: Temporal-Difference learning

Exercise Sheet 3: Signal Detection Theory & Reinforcement Learning

Exercise Sheet 4: Covariance and Correlation, Bayes’ theorem, and Linear discriminant analysis

Exercise Sheet 5: Perceptrons and Hopfield networks

Exercise Sheet 6: integrate-and-Fire Neuron

Exercise Sheet 6: Integrate-and-Fire Neuron

Exercise Sheet 5: Perceptrons and Hopfield networks

Exercise Sheet 6: integrate-and-Fire Neuron

Exercise Sheet 6: Integrate-and-Fire Neuron

Exercise 1: softmax Gibbs-policy

Exercise Sheet 2: Temporal-Difference learning with discounting

Exercise Sheet 2: Temporal-Difference learning

Exercise Sheet 5: Perceptrons and Hopfield networks

Exercise Sheet 4: Covariance and Correlation, Bayes’ theorem, and Linear discriminant analysis

Exercise Sheet 6: integrate-and-Fire Neuron

Exercise Sheet 6: Integrate-and-Fire Neuron

Exercise Sheet 2: Temporal-Difference learning with discounting

Exercise Sheet 2: Temporal-Difference learning

Exercise 1: softmax Gibbs-policy

Exercise Sheet 2: Temporal-Difference learning with discounting

Exercise Sheet 2: Temporal-Difference learning

Exercise Sheet 3: Signal Detection Theory & Reinforcement Learning

Exercise Sheet 4: Covariance and Correlation, Bayes’ theorem, and Linear discriminant analysis

Exercise Sheet 5: Perceptrons and Hopfield networks

Exercise Sheet 6: integrate-and-Fire Neuron

Exercise Sheet 6: Integrate-and-Fire Neuron

Lecture 1: Reinforcement learning

Stimulus $u_i$ Reward $r_i$ Expected reward: $v_i ≝ w u_i$ Prediction error: $δ_i = r_i - v_i$ Loss: $L_i ≝ δ_i^2$

Exercise 1: softmax Gibbs-policy

Exercise Sheet 2: Temporal-Difference learning with discounting

Exercise Sheet 2: Temporal-Difference learning

Lecture 2: Receiver-operating characteristics

Neurons act as if they were performing reinforcement learning.

Lecture 3: Population Coding and Decision-Making

Population Coding: how neural activities relate to an animal’s behavior

Exercise Sheet 3: Signal Detection Theory & Reinforcement Learning

Exercise Sheet 4: Covariance and Correlation, Bayes’ theorem, and Linear discriminant analysis

Lecture 4: Addiction

Addiction: affects all parts of the brain

Lecture 5: The Binary Neuron model

Teacher: Grégory Dumont

Exercise Sheet 5: Perceptrons and Hopfield networks

Lecture 6: Biophysics of neurons

Teacher: Grégory Dumont

Exercise Sheet 6: integrate-and-Fire Neuron

Exercise Sheet 6: Integrate-and-Fire Neuron

Exercise Sheet 4: Covariance and Correlation, Bayes’ theorem, and Linear discriminant analysis

Exercise 1: softmax Gibbs-policy

Exercise Sheet 2: Temporal-Difference learning with discounting

Exercise Sheet 2: Temporal-Difference learning

Exercise Sheet 3: Signal Detection Theory & Reinforcement Learning

Exercise Sheet 4: Covariance and Correlation, Bayes’ theorem, and Linear discriminant analysis

Exercise Sheet 5: Perceptrons and Hopfield networks

Exercise Sheet 6: integrate-and-Fire Neuron

Exercise Sheet 6: Integrate-and-Fire Neuron

Exercise 1: softmax Gibbs-policy

Exercise Sheet 2: Temporal-Difference learning with discounting

Exercise Sheet 2: Temporal-Difference learning

Exercise Sheet 3: Signal Detection Theory & Reinforcement Learning

Exercise Sheet 4: Covariance and Correlation, Bayes’ theorem, and Linear discriminant analysis

Exercise Sheet 5: Perceptrons and Hopfield networks

Exercise Sheet 6: integrate-and-Fire Neuron

Exercise Sheet 6: Integrate-and-Fire Neuron

Exercise Sheet 6: integrate-and-Fire Neuron

Exercise Sheet 6: Integrate-and-Fire Neuron

Lecture 1: Reinforcement learning

Stimulus $u_i$ Reward $r_i$ Expected reward: $v_i ≝ w u_i$ Prediction error: $δ_i = r_i - v_i$ Loss: $L_i ≝ δ_i^2$

Lecture 2: Receiver-operating characteristics

Neurons act as if they were performing reinforcement learning.

Lecture 3: Population Coding and Decision-Making

Population Coding: how neural activities relate to an animal’s behavior

Lecture 4: Addiction

Addiction: affects all parts of the brain

Lecture 5: The Binary Neuron model

Teacher: Grégory Dumont

Lecture 6: Biophysics of neurons

Teacher: Grégory Dumont

Exercise Sheet 5: Perceptrons and Hopfield networks

Lecture 1: Reinforcement learning

Stimulus $u_i$ Reward $r_i$ Expected reward: $v_i ≝ w u_i$ Prediction error: $δ_i = r_i - v_i$ Loss: $L_i ≝ δ_i^2$

Exercise 1: softmax Gibbs-policy

Exercise Sheet 2: Temporal-Difference learning with discounting

Exercise Sheet 2: Temporal-Difference learning

Lecture 2: Receiver-operating characteristics

Neurons act as if they were performing reinforcement learning.

Lecture 3: Population Coding and Decision-Making

Population Coding: how neural activities relate to an animal’s behavior

Exercise Sheet 3: Signal Detection Theory & Reinforcement Learning

Exercise Sheet 4: Covariance and Correlation, Bayes’ theorem, and Linear discriminant analysis

Lecture 4: Addiction

Addiction: affects all parts of the brain

Lecture 5: The Binary Neuron model

Teacher: Grégory Dumont

Exercise Sheet 5: Perceptrons and Hopfield networks

Lecture 6: Biophysics of neurons

Teacher: Grégory Dumont

Exercise Sheet 6: integrate-and-Fire Neuron

Exercise Sheet 6: Integrate-and-Fire Neuron

Exercise Sheet 5: Perceptrons and Hopfield networks

Exercise 1: softmax Gibbs-policy

Exercise Sheet 2: Temporal-Difference learning with discounting

Exercise Sheet 2: Temporal-Difference learning

Exercise Sheet 3: Signal Detection Theory & Reinforcement Learning

Exercise 1: softmax Gibbs-policy

Exercise Sheet 2: Temporal-Difference learning with discounting

Exercise Sheet 2: Temporal-Difference learning

Cours 1 : Introduction

Livres de référence:

Cours 2: Géométrie projective

Trois réglages clefs dans appareil photo:

Cours 3: Matrice d’une caméra

Projets:

TP1: Canny Edges

Practical session 1: Canny Edges

TP2: Optical flow: zero normalized cross-correlations, Lukas Kanade algorithm

Practical session 2: Optical flow

TP4: Neural Networks

Practical session 4: Neural Networks

TP3: Mean Shift

Practical session 3: Mean Shift

Cours 4: Réseaux de neurones

R-CNN

Optional TP: Calibration

Practical session: Calibration

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Optional TP: Calibration

Practical session: Calibration

TP1: Canny Edges

Practical session 1: Canny Edges

TP2: Optical flow: zero normalized cross-correlations, Lukas Kanade algorithm

Practical session 2: Optical flow

TP3: Mean Shift

Practical session 3: Mean Shift

TP4: Neural Networks

Practical session 4: Neural Networks

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Optional TP: Calibration

Practical session: Calibration

Optional TP: Calibration

Practical session: Calibration

Cours 1 : Introduction

Livres de référence:

Cours 2: Géométrie projective

Trois réglages clefs dans appareil photo:

Cours 3: Matrice d’une caméra

Projets:

TP1: Canny Edges

Practical session 1: Canny Edges

TP2: Optical flow: zero normalized cross-correlations, Lukas Kanade algorithm

Practical session 2: Optical flow

TP4: Neural Networks

Practical session 4: Neural Networks

TP3: Mean Shift

Practical session 3: Mean Shift

Cours 4: Réseaux de neurones

R-CNN

Optional TP: Calibration

Practical session: Calibration

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Cours 1 : Introduction

Livres de référence:

Cours 2: Géométrie projective

Trois réglages clefs dans appareil photo:

Cours 3: Matrice d’une caméra

Projets:

TP1: Canny Edges

Practical session 1: Canny Edges

TP2: Optical flow: zero normalized cross-correlations, Lukas Kanade algorithm

Practical session 2: Optical flow

TP4: Neural Networks

Practical session 4: Neural Networks

TP3: Mean Shift

Practical session 3: Mean Shift

Cours 4: Réseaux de neurones

R-CNN

Optional TP: Calibration

Practical session: Calibration

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

TP1: Canny Edges

Practical session 1: Canny Edges

TP2: Optical flow: zero normalized cross-correlations, Lukas Kanade algorithm

Practical session 2: Optical flow

TP4: Neural Networks

Practical session 4: Neural Networks

TP3: Mean Shift

Practical session 3: Mean Shift

Optional TP: Calibration

Practical session: Calibration

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Cours 1 : Introduction

Livres de référence:

Cours 2: Géométrie projective

Trois réglages clefs dans appareil photo:

Cours 3: Matrice d’une caméra

Projets:

TP1: Canny Edges

Practical session 1: Canny Edges

TP2: Optical flow: zero normalized cross-correlations, Lukas Kanade algorithm

Practical session 2: Optical flow

TP4: Neural Networks

Practical session 4: Neural Networks

TP3: Mean Shift

Practical session 3: Mean Shift

Cours 4: Réseaux de neurones

R-CNN

Optional TP: Calibration

Practical session: Calibration

Cours 4: Réseaux de neurones

R-CNN

Cours 1 : Introduction

Livres de référence:

Cours 2: Géométrie projective

Trois réglages clefs dans appareil photo:

Cours 3: Matrice d’une caméra

Projets:

TP4: Neural Networks

Practical session 4: Neural Networks

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

TP4: Neural Networks

Practical session 4: Neural Networks

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

TP2: Optical flow: zero normalized cross-correlations, Lukas Kanade algorithm

Practical session 2: Optical flow

TP4: Neural Networks

Practical session 4: Neural Networks

TP3: Mean Shift

Practical session 3: Mean Shift

Optional TP: Calibration

Practical session: Calibration

TP1: Canny Edges

Practical session 1: Canny Edges

Optional TP: Calibration

Practical session: Calibration

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Final Summary: Dimensionality Reduction and Visualization of Representations

Final Summary for the final presentation

Pospaces, Local pospaces

Recap: every program in our setting is comprised of sequential processes $(P_1, …, P_n)$ that are turned into a control flow graph $(G_1, …, G_n)$

Metric spaces, Pospaces and Local pospaces

Metric Spaces

Geometric models

Cartesian product

Isothetic regions

Directed Algebraic Topology and Concurrency: Introduction

Teacher: Emmanuel Haucourt

Directed Algebraic Topology and Concurrency: Introduction

Teacher: Emmanuel Haucourt

Pospaces, Local pospaces

Recap: every program in our setting is comprised of sequential processes $(P_1, …, P_n)$ that are turned into a control flow graph $(G_1, …, G_n)$

Metric spaces, Pospaces and Local pospaces

Metric Spaces

Geometric models

Cartesian product

Calcul des séquents

TD 5 : Sémantique de Kripke

Énoncé

DM : QBF et la logique intuitionniste

Logic Project : SAT solver

Logic Project : SAT solver

Naive and DPLL algorithms

Logic Project : SAT solver

Naive and DPLL algorithms

EX 1.

Theories

DM : QBF et la logique intuitionniste

Cours 2 : Logique intuitionniste et sémantique de Kripke

Calcul des séquents

TD 4 : Systèmes de preuve classique et inuitionniste

Énoncé

TD 5 : Sémantique de Kripke

Énoncé

Lecture 2: Perception en robotique

David Filliat

Lecture 3: Neurorobotic models of spacial cognition

Teacher: Anfelo Arleo

Lecture 4: Neuromimetic Navigation Strategies

Teacher: Benoît Girard

Lecture 5: Bayesian filters

Teacher: Bessière Pierre

Lecture 6: Perception et émotion pour l’interaction

Teacher: Philippe Gaussier

Tutorial 2: Navigation Strategies

Lecture 7: Motor Control

Teacher: Emmanuel Guigon (ISIR, Sorbonne Université)

Lecture 8: Evolutionary Robotics

Teacher: Stéphane Doncieux, Nicolas Bredèche

Lecture 1: Neurorobotics

La robotique peut aider les neurosciences :

[Final Project] Inferring Space from Sensorimotor Dependencies

Documentation Slides iPython notebook

Tutorial 3: Regression

Tutorial 2: Navigation Strategies

Tutorial 4: Intent Recognition

Lab 4: Intent Recognition Younesse Kaddar, Alexandre Olech and Kexin Ren (Lecturer: Mohamed Chetouani)

Lecture 1: Neurorobotics

La robotique peut aider les neurosciences :

Lecture 2: Perception en robotique

David Filliat

Lecture 3: Neurorobotic models of spacial cognition

Teacher: Anfelo Arleo

Lecture 4: Neuromimetic Navigation Strategies

Teacher: Benoît Girard

Lecture 5: Bayesian filters

Teacher: Bessière Pierre

Lecture 6: Perception et émotion pour l’interaction

Teacher: Philippe Gaussier

Lecture 7: Motor Control

Teacher: Emmanuel Guigon (ISIR, Sorbonne Université)

Lecture 8: Evolutionary Robotics

Teacher: Stéphane Doncieux, Nicolas Bredèche

Tutorial 1: Reinforcement Learning

Tutorial 2: Navigation Strategies

Tutorial 3: Regression

Tutorial 3: Regression

Tutorial 4: Intent Recognition

Lab 4: Intent Recognition Younesse Kaddar, Alexandre Olech and Kexin Ren (Lecturer: Mohamed Chetouani)

Lecture 1: Neurorobotics

La robotique peut aider les neurosciences :

Lecture 2: Perception en robotique

David Filliat

Lecture 3: Neurorobotic models of spacial cognition

Teacher: Anfelo Arleo

Lecture 4: Neuromimetic Navigation Strategies

Teacher: Benoît Girard

Lecture 5: Bayesian filters

Teacher: Bessière Pierre

Lecture 6: Perception et émotion pour l’interaction

Teacher: Philippe Gaussier

Lecture 7: Motor Control

Teacher: Emmanuel Guigon (ISIR, Sorbonne Université)

Lecture 8: Evolutionary Robotics

Teacher: Stéphane Doncieux, Nicolas Bredèche

Lecture 7: Motor Control

Teacher: Emmanuel Guigon (ISIR, Sorbonne Université)

Lecture 8: Evolutionary Robotics

Teacher: Stéphane Doncieux, Nicolas Bredèche

Tutorial 2: Navigation Strategies

Lecture 1: Neurorobotics

La robotique peut aider les neurosciences :

Lecture 2: Perception en robotique

David Filliat

Tutorial 1: Reinforcement Learning

Lecture 3: Neurorobotic models of spacial cognition

Teacher: Anfelo Arleo

Lecture 4: Neuromimetic Navigation Strategies

Teacher: Benoît Girard

Lecture 5: Bayesian filters

Teacher: Bessière Pierre

Tutorial 2: Navigation Strategies

Tutorial 3: Regression

Lecture 6: Perception et émotion pour l’interaction

Teacher: Philippe Gaussier

Lecture 7: Motor Control

Teacher: Emmanuel Guigon (ISIR, Sorbonne Université)

Lecture 8: Evolutionary Robotics

Teacher: Stéphane Doncieux, Nicolas Bredèche

[Final Project] Inferring Space from Sensorimotor Dependencies

Documentation Slides iPython notebook

Lecture 1: Neurorobotics

La robotique peut aider les neurosciences :

Lecture 2: Perception en robotique

David Filliat

Tutorial 1: Reinforcement Learning

Lecture 3: Neurorobotic models of spacial cognition

Teacher: Anfelo Arleo

Lecture 4: Neuromimetic Navigation Strategies

Teacher: Benoît Girard

Lecture 5: Bayesian filters

Teacher: Bessière Pierre

Tutorial 2: Navigation Strategies

Tutorial 3: Regression

Lecture 6: Perception et émotion pour l’interaction

Teacher: Philippe Gaussier

Lecture 7: Motor Control

Teacher: Emmanuel Guigon (ISIR, Sorbonne Université)

Lecture 8: Evolutionary Robotics

Teacher: Stéphane Doncieux, Nicolas Bredèche

[Final Project] Inferring Space from Sensorimotor Dependencies

Documentation Slides iPython notebook

Lecture 1: Neurorobotics

La robotique peut aider les neurosciences :

Lecture 2: Perception en robotique

David Filliat

Tutorial 1: Reinforcement Learning

Lecture 3: Neurorobotic models of spacial cognition

Teacher: Anfelo Arleo

Lecture 4: Neuromimetic Navigation Strategies

Teacher: Benoît Girard

Lecture 5: Bayesian filters

Teacher: Bessière Pierre

Tutorial 2: Navigation Strategies

Tutorial 3: Regression

Lecture 6: Perception et émotion pour l’interaction

Teacher: Philippe Gaussier

Lecture 7: Motor Control

Teacher: Emmanuel Guigon (ISIR, Sorbonne Université)

Lecture 8: Evolutionary Robotics

Teacher: Stéphane Doncieux, Nicolas Bredèche

[Final Project] Inferring Space from Sensorimotor Dependencies

Documentation Slides iPython notebook

Tutorial 1: Reinforcement Learning

[Final Project] Inferring Space from Sensorimotor Dependencies

Documentation Slides iPython notebook

Tutorial 3: Regression

Tutorial 1: Reinforcement Learning

Tutorial 2: Navigation Strategies

Lecture 1: Neurorobotics

La robotique peut aider les neurosciences :

Lecture 2: Perception en robotique

David Filliat

Tutorial 1: Reinforcement Learning

Lecture 3: Neurorobotic models of spacial cognition

Teacher: Anfelo Arleo

Lecture 4: Neuromimetic Navigation Strategies

Teacher: Benoît Girard

Lecture 5: Bayesian filters

Teacher: Bessière Pierre

Tutorial 2: Navigation Strategies

Tutorial 3: Regression

Lecture 6: Perception et émotion pour l’interaction

Teacher: Philippe Gaussier

Lecture 7: Motor Control

Teacher: Emmanuel Guigon (ISIR, Sorbonne Université)

Lecture 8: Evolutionary Robotics

Teacher: Stéphane Doncieux, Nicolas Bredèche

[Final Project] Inferring Space from Sensorimotor Dependencies

Documentation Slides iPython notebook

Lecture 1: Neurorobotics

La robotique peut aider les neurosciences :

Lecture 2: Perception en robotique

David Filliat

Lecture 3: Neurorobotic models of spacial cognition

Teacher: Anfelo Arleo

Lecture 4: Neuromimetic Navigation Strategies

Teacher: Benoît Girard

Lecture 5: Bayesian filters

Teacher: Bessière Pierre

Lecture 6: Perception et émotion pour l’interaction

Teacher: Philippe Gaussier

Lecture 7: Motor Control

Teacher: Emmanuel Guigon (ISIR, Sorbonne Université)

Lecture 8: Evolutionary Robotics

Teacher: Stéphane Doncieux, Nicolas Bredèche

Tutorial 3: Regression

Tutorial 4: Intent Recognition

Lab 4: Intent Recognition Younesse Kaddar, Alexandre Olech and Kexin Ren (Lecturer: Mohamed Chetouani)

Tutorial 1: Reinforcement Learning

Tutorial 2: Navigation Strategies

Tutorial 3: Regression

[Final Project] Inferring Space from Sensorimotor Dependencies

Documentation Slides iPython notebook

Tutorial 4: Intent Recognition

Lab 4: Intent Recognition Younesse Kaddar, Alexandre Olech and Kexin Ren (Lecturer: Mohamed Chetouani)

UE Cognition Conférence: Penser

Introduction : qu’est-ce que c’est que penser ?

Cours 1: Introduction

Neurosciences: introduction

Cours 2: Moelle spinale, Tronc cérébral

Moelle spinale

Cours 3: Coupes coronales sériées

Coupes coronales sériées

UE Cognition Conférence: Penser

Introduction : qu’est-ce que c’est que penser ?

Cours 1: Introduction

Neurosciences: introduction

Cours 2: Moelle spinale, Tronc cérébral

Moelle spinale

Cours 3: Coupes coronales sériées

Coupes coronales sériées

Cours 1: Introduction

Neurosciences: introduction

Cours 2: Moelle spinale, Tronc cérébral

Moelle spinale

Cours 3: Coupes coronales sériées

Coupes coronales sériées

Cours 1: Introduction

Neurosciences: introduction

Cours 2: Moelle spinale, Tronc cérébral

Moelle spinale

Cours 3: Coupes coronales sériées

Coupes coronales sériées

UE Cognition Conférence: Penser

Introduction : qu’est-ce que c’est que penser ?

Cours 1: Introduction

Neurosciences: introduction

Cours 2: Moelle spinale, Tronc cérébral

Moelle spinale

Cours 3: Coupes coronales sériées

Coupes coronales sériées

UE Cognition Conférence: Penser

Introduction : qu’est-ce que c’est que penser ?

Cours 1: Introduction

Neurosciences: introduction

Cours 2: Moelle spinale, Tronc cérébral

Moelle spinale

Cours 3: Coupes coronales sériées

Coupes coronales sériées

Introductive tutorial: Evolution of a population

Introductive tutorial

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

[Problem Set 2 Quantitative models of behavior] Problem 2: Decision strategy for flower sampling by bees

Problem 2: Decision strategy for flower sampling by bees.

[Problem Set 2 Quantitative models of behavior] Problem 3: The drift diffusion model of decision-making

Problem 3: The drift diffusion model of decision-making.

[Problem Set 2 Quantitative models of behavior] Problem 4: Reinforcement learning in a maze

Problem 4: Reinforcement learning in a maze.

Problem Set 3: Spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 1: Poisson spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 2: Analysis of spike trains

Problem 2: Analysis of spike train.

[Problem Set 3 Spike trains] Problem 3: Integrate-and-Fire neuron

Problem 3: Integrate-and-Fire neuron.

[Problem Set 3 Spike trains] Problem 4: The Hodgkin-Huxley model

Problem 4: the Hodgkin-Huxley model.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

Lecture 1: Neuromodeling

Manuel Beiran: manuel.beiran-at-ens.fr

Lecture 2: Rescorla-Wagner Rule

Classical conditioning

Lecture 3: Exploration-exploitation dilemma

Computational model of behavior:

Lecture 4: Drift-diffusion models

Reminder: differential equations we’ll encounter

Lecture 5: Integrate-and-fire neurons

A Cell ⟺ An RC circuit

Lecture 6: Hodgkin-Huxley model

Cell membrane = semi-permeable

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

[Problem Set 3 Spike trains] Problem 4: The Hodgkin-Huxley model

Problem 4: the Hodgkin-Huxley model.

Lecture 6: Hodgkin-Huxley model

Cell membrane = semi-permeable

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

[Problem Set 3 Spike trains] Problem 3: Integrate-and-Fire neuron

Problem 3: Integrate-and-Fire neuron.

Lecture 5: Integrate-and-fire neurons

A Cell ⟺ An RC circuit

Problem Set 3: Spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 1: Poisson spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 2: Analysis of spike trains

Problem 2: Analysis of spike train.

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

[Problem Set 2 Quantitative models of behavior] Problem 4: Reinforcement learning in a maze

Problem 4: Reinforcement learning in a maze.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

Lecture 1: Neuromodeling

Manuel Beiran: manuel.beiran-at-ens.fr

Lecture 2: Rescorla-Wagner Rule

Classical conditioning

Lecture 3: Exploration-exploitation dilemma

Computational model of behavior:

Lecture 4: Drift-diffusion models

Reminder: differential equations we’ll encounter

Lecture 5: Integrate-and-fire neurons

A Cell ⟺ An RC circuit

Lecture 6: Hodgkin-Huxley model

Cell membrane = semi-permeable

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 3: The drift diffusion model of decision-making

Problem 3: The drift diffusion model of decision-making.

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 3: The drift diffusion model of decision-making

Problem 3: The drift diffusion model of decision-making.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

Introductive tutorial: Evolution of a population

Introductive tutorial

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

[Problem Set 2 Quantitative models of behavior] Problem 2: Decision strategy for flower sampling by bees

Problem 2: Decision strategy for flower sampling by bees.

[Problem Set 2 Quantitative models of behavior] Problem 3: The drift diffusion model of decision-making

Problem 3: The drift diffusion model of decision-making.

[Problem Set 2 Quantitative models of behavior] Problem 4: Reinforcement learning in a maze

Problem 4: Reinforcement learning in a maze.

Problem Set 3: Spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 1: Poisson spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 2: Analysis of spike trains

Problem 2: Analysis of spike train.

[Problem Set 3 Spike trains] Problem 3: Integrate-and-Fire neuron

Problem 3: Integrate-and-Fire neuron.

[Problem Set 3 Spike trains] Problem 4: The Hodgkin-Huxley model

Problem 4: the Hodgkin-Huxley model.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

[Problem Set 3 Spike trains] Problem 3: Integrate-and-Fire neuron

Problem 3: Integrate-and-Fire neuron.

Lecture 5: Integrate-and-fire neurons

A Cell ⟺ An RC circuit

Introductive tutorial: Evolution of a population

Introductive tutorial

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

[Problem Set 2 Quantitative models of behavior] Problem 2: Decision strategy for flower sampling by bees

Problem 2: Decision strategy for flower sampling by bees.

[Problem Set 2 Quantitative models of behavior] Problem 3: The drift diffusion model of decision-making

Problem 3: The drift diffusion model of decision-making.

[Problem Set 2 Quantitative models of behavior] Problem 4: Reinforcement learning in a maze

Problem 4: Reinforcement learning in a maze.

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

[Problem Set 2 Quantitative models of behavior] Problem 2: Decision strategy for flower sampling by bees

Problem 2: Decision strategy for flower sampling by bees.

[Problem Set 2 Quantitative models of behavior] Problem 3: The drift diffusion model of decision-making

Problem 3: The drift diffusion model of decision-making.

[Problem Set 2 Quantitative models of behavior] Problem 4: Reinforcement learning in a maze

Problem 4: Reinforcement learning in a maze.

Problem Set 3: Spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 1: Poisson spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 2: Analysis of spike trains

Problem 2: Analysis of spike train.

[Problem Set 3 Spike trains] Problem 3: Integrate-and-Fire neuron

Problem 3: Integrate-and-Fire neuron.

[Problem Set 3 Spike trains] Problem 4: The Hodgkin-Huxley model

Problem 4: the Hodgkin-Huxley model.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

Lecture 1: Neuromodeling

Manuel Beiran: manuel.beiran-at-ens.fr

Lecture 2: Rescorla-Wagner Rule

Classical conditioning

Lecture 3: Exploration-exploitation dilemma

Computational model of behavior:

Lecture 4: Drift-diffusion models

Reminder: differential equations we’ll encounter

Lecture 5: Integrate-and-fire neurons

A Cell ⟺ An RC circuit

Lecture 6: Hodgkin-Huxley model

Cell membrane = semi-permeable

Lecture 6: Hodgkin-Huxley model

Cell membrane = semi-permeable

Lecture 1: Neuromodeling

Manuel Beiran: manuel.beiran-at-ens.fr

Lecture 2: Rescorla-Wagner Rule

Classical conditioning

Lecture 3: Exploration-exploitation dilemma

Computational model of behavior:

Lecture 4: Drift-diffusion models

Reminder: differential equations we’ll encounter

Lecture 5: Integrate-and-fire neurons

A Cell ⟺ An RC circuit

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

Lecture 6: Hodgkin-Huxley model

Cell membrane = semi-permeable

Lecture 1: Neuromodeling

Manuel Beiran: manuel.beiran-at-ens.fr

Lecture 2: Rescorla-Wagner Rule

Classical conditioning

Lecture 3: Exploration-exploitation dilemma

Computational model of behavior:

Lecture 4: Drift-diffusion models

Reminder: differential equations we’ll encounter

Lecture 5: Integrate-and-fire neurons

A Cell ⟺ An RC circuit

Introductive tutorial: Evolution of a population

Introductive tutorial

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

[Problem Set 2 Quantitative models of behavior] Problem 2: Decision strategy for flower sampling by bees

Problem 2: Decision strategy for flower sampling by bees.

[Problem Set 2 Quantitative models of behavior] Problem 3: The drift diffusion model of decision-making

Problem 3: The drift diffusion model of decision-making.

[Problem Set 2 Quantitative models of behavior] Problem 4: Reinforcement learning in a maze

Problem 4: Reinforcement learning in a maze.

Problem Set 3: Spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 1: Poisson spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 2: Analysis of spike trains

Problem 2: Analysis of spike train.

[Problem Set 3 Spike trains] Problem 3: Integrate-and-Fire neuron

Problem 3: Integrate-and-Fire neuron.

[Problem Set 3 Spike trains] Problem 4: The Hodgkin-Huxley model

Problem 4: the Hodgkin-Huxley model.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

Introductive tutorial: Evolution of a population

Introductive tutorial

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

[Problem Set 2 Quantitative models of behavior] Problem 2: Decision strategy for flower sampling by bees

Problem 2: Decision strategy for flower sampling by bees.

[Problem Set 2 Quantitative models of behavior] Problem 3: The drift diffusion model of decision-making

Problem 3: The drift diffusion model of decision-making.

[Problem Set 2 Quantitative models of behavior] Problem 4: Reinforcement learning in a maze

Problem 4: Reinforcement learning in a maze.

Problem Set 3: Spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 1: Poisson spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 2: Analysis of spike trains

Problem 2: Analysis of spike train.

[Problem Set 3 Spike trains] Problem 3: Integrate-and-Fire neuron

Problem 3: Integrate-and-Fire neuron.

[Problem Set 3 Spike trains] Problem 4: The Hodgkin-Huxley model

Problem 4: the Hodgkin-Huxley model.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

Lecture 1: Neuromodeling

Manuel Beiran: manuel.beiran-at-ens.fr

Lecture 2: Rescorla-Wagner Rule

Classical conditioning

Lecture 3: Exploration-exploitation dilemma

Computational model of behavior:

Lecture 4: Drift-diffusion models

Reminder: differential equations we’ll encounter

Lecture 5: Integrate-and-fire neurons

A Cell ⟺ An RC circuit

Lecture 6: Hodgkin-Huxley model

Cell membrane = semi-permeable

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

Introductive tutorial: Evolution of a population

Introductive tutorial

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

[Problem Set 2 Quantitative models of behavior] Problem 2: Decision strategy for flower sampling by bees

Problem 2: Decision strategy for flower sampling by bees.

[Problem Set 2 Quantitative models of behavior] Problem 3: The drift diffusion model of decision-making

Problem 3: The drift diffusion model of decision-making.

[Problem Set 2 Quantitative models of behavior] Problem 4: Reinforcement learning in a maze

Problem 4: Reinforcement learning in a maze.

Problem Set 3: Spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 1: Poisson spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 2: Analysis of spike trains

Problem 2: Analysis of spike train.

[Problem Set 3 Spike trains] Problem 3: Integrate-and-Fire neuron

Problem 3: Integrate-and-Fire neuron.

[Problem Set 3 Spike trains] Problem 4: The Hodgkin-Huxley model

Problem 4: the Hodgkin-Huxley model.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

[Project] Coherent Patterns of Activity from Chaotic Neural Networks

Final Project: Coherent Patterns of Activity from Chaotic Neural Networks.

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 2: Decision strategy for flower sampling by bees

Problem 2: Decision strategy for flower sampling by bees.

[Problem Set 2 Quantitative models of behavior] Problem 4: Reinforcement learning in a maze

Problem 4: Reinforcement learning in a maze.

[Problem Set 2 Quantitative models of behavior] Problem 2: Decision strategy for flower sampling by bees

Problem 2: Decision strategy for flower sampling by bees.

Problem Set 3: Spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 1: Poisson spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 2: Analysis of spike trains

Problem 2: Analysis of spike train.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

Introductive tutorial: Evolution of a population

Introductive tutorial

Problem Set 2: Quantitative models of behavior

Problem Set 2: Quantitative models of behavior.

[Problem Set 2 Quantitative models of behavior] Problem 1: Rescola-Wagner Rule

Problem 1: The Rescola-Wagner Rule

[Problem Set 2 Quantitative models of behavior] Problem 2: Decision strategy for flower sampling by bees

Problem 2: Decision strategy for flower sampling by bees.

[Problem Set 2 Quantitative models of behavior] Problem 3: The drift diffusion model of decision-making

Problem 3: The drift diffusion model of decision-making.

[Problem Set 2 Quantitative models of behavior] Problem 4: Reinforcement learning in a maze

Problem 4: Reinforcement learning in a maze.

Problem Set 3: Spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 1: Poisson spike trains

Problem 1: Poisson spike trains.

[Problem Set 3 Spike trains] Problem 2: Analysis of spike trains

Problem 2: Analysis of spike train.

[Problem Set 3 Spike trains] Problem 3: Integrate-and-Fire neuron

Problem 3: Integrate-and-Fire neuron.

[Problem Set 3 Spike trains] Problem 4: The Hodgkin-Huxley model

Problem 4: the Hodgkin-Huxley model.

Problem Set 4: Networks

Problem Set 4: Networks.

[Problem Set 4 Networks] Problem 1: Neuron with Autapse

Problem 1: Neuron with Autapse.

[Problem Set 4 Networks] Problem 2: Circuit with mutual inhibition

Problem 2: Circuit with mutual inhibition.

[Problem Set 4 Networks] Problem 3: Hopfield model

Problem 3: Hopfield model.

TIPE 2015 : Mouvement Brownien et Détermination du nombre d’Avogadro

Détermination du nombre d’Avogadro

TIPE 2016 Résumé : Théorie des modèles

MathJax + Version PDF

TIPE 2016 Transparents : Théorie des modèles

MathJax + Version PDF

TIPE 2016 : THÉORIE DES MODÈLES, DE L’EXEMPLE AU MODÈLE (Version Exhaustive)

MathJax + Version PDF

TIPE 2015 : Mouvement Brownien et Détermination du nombre d’Avogadro

Détermination du nombre d’Avogadro

TIPE 2015 : Mouvement Brownien et Détermination du nombre d’Avogadro

Détermination du nombre d’Avogadro

Système différentiel : orbites

MathJax + Version PDF

Algèbre Linéaire : Valeurs propres imaginaires pures

MathJax + Version PDF

TIPE 2015 : Mouvement Brownien et Détermination du nombre d’Avogadro

Détermination du nombre d’Avogadro

TIPE 2016 Résumé : Théorie des modèles

MathJax + Version PDF

TIPE 2016 Transparents : Théorie des modèles

MathJax + Version PDF

TIPE 2016 : THÉORIE DES MODÈLES, DE L’EXEMPLE AU MODÈLE (Version Exhaustive)

MathJax + Version PDF

Probabilités : Loi hypergéométrique

MathJax + Version PDF

Analyse : Fonction de McCarthy

Version PDF

Algèbre Linéaire : Valeurs propres imaginaires pures

MathJax + Version PDF

Probabilités : Loi hypergéométrique

MathJax + Version PDF

Analyse : Fonction de McCarthy

Version PDF

Lemme : fonction uniformément continue qui admet une intégrale

MathJax + Version PDF

Système différentiel : orbites

MathJax + Version PDF

TIPE 2015 : Mouvement Brownien et Détermination du nombre d’Avogadro

Détermination du nombre d’Avogadro

TIPE 2015 : Mouvement Brownien et Détermination du nombre d’Avogadro

Détermination du nombre d’Avogadro

TIPE 2015 : Mouvement Brownien et Détermination du nombre d’Avogadro

Détermination du nombre d’Avogadro

TIPE 2016 Résumé : Théorie des modèles

MathJax + Version PDF

TIPE 2016 Transparents : Théorie des modèles

MathJax + Version PDF

TIPE 2016 : THÉORIE DES MODÈLES, DE L’EXEMPLE AU MODÈLE (Version Exhaustive)

MathJax + Version PDF

TIPE 2016 Résumé : Théorie des modèles

MathJax + Version PDF

TIPE 2016 Transparents : Théorie des modèles

MathJax + Version PDF

TIPE 2016 : THÉORIE DES MODÈLES, DE L’EXEMPLE AU MODÈLE (Version Exhaustive)

MathJax + Version PDF

TIPE 2016 Résumé : Théorie des modèles

MathJax + Version PDF

TIPE 2016 Transparents : Théorie des modèles

MathJax + Version PDF

TIPE 2016 : THÉORIE DES MODÈLES, DE L’EXEMPLE AU MODÈLE (Version Exhaustive)

MathJax + Version PDF

Algèbre Linéaire : Valeurs propres imaginaires pures

MathJax + Version PDF

Analyse : Fonction de McCarthy

Version PDF

Lemme : fonction uniformément continue qui admet une intégrale

MathJax + Version PDF

Analyse : Fonction de McCarthy

Version PDF

Lemme : fonction uniformément continue qui admet une intégrale

MathJax + Version PDF

Analyse : Fonction de McCarthy

Version PDF

Algèbre Linéaire : Valeurs propres imaginaires pures

MathJax + Version PDF

Probabilités : Loi hypergéométrique

MathJax + Version PDF

Analyse : Fonction de McCarthy

Version PDF

Lemme : fonction uniformément continue qui admet une intégrale

MathJax + Version PDF

Système différentiel : orbites

MathJax + Version PDF

Lemme : fonction uniformément continue qui admet une intégrale

MathJax + Version PDF

TIPE 2016 Résumé : Théorie des modèles

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TIPE 2016 Transparents : Théorie des modèles

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TIPE 2016 : THÉORIE DES MODÈLES, DE L’EXEMPLE AU MODÈLE (Version Exhaustive)

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TIPE 2016 Résumé : Théorie des modèles

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TIPE 2016 Transparents : Théorie des modèles

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TIPE 2016 : THÉORIE DES MODÈLES, DE L’EXEMPLE AU MODÈLE (Version Exhaustive)

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Algèbre Linéaire : Valeurs propres imaginaires pures

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Probabilités : Loi hypergéométrique

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Analyse : Fonction de McCarthy

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Lemme : fonction uniformément continue qui admet une intégrale

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Système différentiel : orbites

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TIPE 2016 Résumé : Théorie des modèles

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TIPE 2016 Transparents : Théorie des modèles

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TIPE 2016 : THÉORIE DES MODÈLES, DE L’EXEMPLE AU MODÈLE (Version Exhaustive)

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TIPE 2015 : Mouvement Brownien et Détermination du nombre d’Avogadro

Détermination du nombre d’Avogadro

Algèbre Linéaire : Valeurs propres imaginaires pures

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Système différentiel : orbites

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Système différentiel : orbites

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TIPE 2015 : Mouvement Brownien et Détermination du nombre d’Avogadro

Détermination du nombre d’Avogadro

Probabilités : Loi hypergéométrique

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Probabilités : Loi hypergéométrique

MathJax + Version PDF

Système différentiel : orbites

MathJax + Version PDF

Analyse : Fonction de McCarthy

Version PDF

TIPE 2016 Résumé : Théorie des modèles

MathJax + Version PDF

TIPE 2016 Transparents : Théorie des modèles

MathJax + Version PDF

TIPE 2016 : THÉORIE DES MODÈLES, DE L’EXEMPLE AU MODÈLE (Version Exhaustive)

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Lemme : fonction uniformément continue qui admet une intégrale

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Algèbre Linéaire : Valeurs propres imaginaires pures

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[Final Coq Project] Coherence of Heyting’s arithmetic

Coherence of Heyting’s arithmetic in Coq

[Final Coq Project] Coherence of Heyting’s arithmetic

Coherence of Heyting’s arithmetic in Coq

Dependent types and HOL

Dependent Types

HOL

Symmetry in HOL

[Final Coq Project] Coherence of Heyting’s arithmetic

Coherence of Heyting’s arithmetic in Coq

Isabelle

Isabelle (named by Lawrence Paulson after the daughter of Gérard Huet, as a tribute): a popular generic theorem prover

HOL

Symmetry in HOL

Isabelle

Isabelle (named by Lawrence Paulson after the daughter of Gérard Huet, as a tribute): a popular generic theorem prover

TD10: Damier (checker)

NB: Boards are finite. Number of configurations:

[Final Coq Project] Coherence of Heyting’s arithmetic

Coherence of Heyting’s arithmetic in Coq

TD8: Définitions inductives de prédicats

Page du cours

TD9: Enigme de MU: Définitions inductives de prédicats et analyse par cas

TD9 : Définitions inductives de prédicats et analyse par cas

TD10: Damier (checker)

NB: Boards are finite. Number of configurations:

[Final Coq Project] Coherence of Heyting’s arithmetic

Coherence of Heyting’s arithmetic in Coq

[Final Coq Project] Coherence of Heyting’s arithmetic

Coherence of Heyting’s arithmetic in Coq

Dependent types and HOL

Dependent Types

HOL

Symmetry in HOL

Isabelle

Isabelle (named by Lawrence Paulson after the daughter of Gérard Huet, as a tribute): a popular generic theorem prover

Dependent types and HOL

Dependent Types

TD8: Définitions inductives de prédicats

Page du cours

HOL

Symmetry in HOL

TD9: Enigme de MU: Définitions inductives de prédicats et analyse par cas

TD9 : Définitions inductives de prédicats et analyse par cas

Isabelle

Isabelle (named by Lawrence Paulson after the daughter of Gérard Huet, as a tribute): a popular generic theorem prover

TD10: Damier (checker)

NB: Boards are finite. Number of configurations:

Dependent types and HOL

Dependent Types

Dependent types and HOL

Dependent Types

HOL

Symmetry in HOL

Isabelle

Isabelle (named by Lawrence Paulson after the daughter of Gérard Huet, as a tribute): a popular generic theorem prover

[Final Coq Project] Coherence of Heyting’s arithmetic

Coherence of Heyting’s arithmetic in Coq

Dependent types and HOL

Dependent Types

HOL

Symmetry in HOL

Isabelle

Isabelle (named by Lawrence Paulson after the daughter of Gérard Huet, as a tribute): a popular generic theorem prover

[Final Coq Project] Coherence of Heyting’s arithmetic

Coherence of Heyting’s arithmetic in Coq

TD8: Définitions inductives de prédicats

Page du cours

TD9: Enigme de MU: Définitions inductives de prédicats et analyse par cas

TD9 : Définitions inductives de prédicats et analyse par cas

TD10: Damier (checker)

NB: Boards are finite. Number of configurations:

[Final Coq Project] Coherence of Heyting’s arithmetic

Coherence of Heyting’s arithmetic in Coq

Cours 1: Algèbre initiale

Algèbre intiale (pour un foncteur)

Cours 2: cohérence de $PA_2$, Girard et Takeuti

Imprédicativité:

Cours 3: Candidats de réductibilité de Tait

Comment pallier le problème de définition circulaire des candidats de réductibilité $Red(∀X. A)$ ?

Cours 5: Sémantique dénotationnelle

Introduction

Cours 6: Domaines de Scott

Dans une Catégorie Cartésienne Close (CCC)

Cours 7: Espace cohérents

Espaces cohérents

Cours 8: La catégories des espaces cohérents est monoïdale close

Catégories $Coh$ et $Stab$

Cours 9: Logique linéaire

Rappel: Le bang $!: Stab ⟶ Coh$ est l’adjoint à gauche du foncteur d’oubli $𝒰: Coh ⟶ Stab$:

Cours 10: Traduction de Girard et Substitution explicite

Traduction de Girard

Cours 11: Réseaux de preuves

Réseaux de preuves pour MLL

Cours 12: Axiomatisation catégorique de la logique linéaire

Rappels

Cours 4: Normalisation du système F

Expression, dans Peano, du fait que $Red_ρ(A)$ est un candidat de réductibilité:

TD: La catégorie $PCoh$

1. Le produit monoïdal $\otimes$ de $PCoh$

Cours 13: Théorème de Friedman: correction

Nouveau professeur pour cette seconde partie : Michele Pagani

Cours 14: Théorème de Friedman: complétude

Friedman:

Cours 15: PCF

Expressivité

Cours 16: PCF: Logical Relations to show Adequacy

Rappels

Cours 17: Fully abstract models

Th: Let $M$ be a PCF program, then \[⟦M⟧ = n \text{ iff } M ⟶^\ast \underline{n}\]

Cours 18: Catégorie des Espaces Cohérents: Modèle de PCF

cf. Linear Logic, Girard (1987)

Cours 19: Probabilistic Coherence Spaces and the $PCoh$ category

Caractérisation des espaces cohérents

Cours 20: Probabilistic Coherence Spaces and Linear Logic

In Probabilistic Coherence Spaces, $𝒫(𝒜)$ is closed under $\sup$

TD: La catégorie $PCoh$

1. Le produit monoïdal $\otimes$ de $PCoh$

TD: La catégorie $PCoh$

1. Le produit monoïdal $\otimes$ de $PCoh$

TD: La catégorie $PCoh$

1. Le produit monoïdal $\otimes$ de $PCoh$

Cours 1: Algèbre initiale

Algèbre intiale (pour un foncteur)

Cours 2: cohérence de $PA_2$, Girard et Takeuti

Imprédicativité:

Cours 3: Candidats de réductibilité de Tait

Comment pallier le problème de définition circulaire des candidats de réductibilité $Red(∀X. A)$ ?

Cours 4: Normalisation du système F

Expression, dans Peano, du fait que $Red_ρ(A)$ est un candidat de réductibilité:

Cours 5: Sémantique dénotationnelle

Introduction

Cours 6: Domaines de Scott

Dans une Catégorie Cartésienne Close (CCC)

Cours 7: Espace cohérents

Espaces cohérents

Cours 8: La catégories des espaces cohérents est monoïdale close

Catégories $Coh$ et $Stab$

Cours 9: Logique linéaire

Rappel: Le bang $!: Stab ⟶ Coh$ est l’adjoint à gauche du foncteur d’oubli $𝒰: Coh ⟶ Stab$:

Cours 10: Traduction de Girard et Substitution explicite

Traduction de Girard

Cours 11: Réseaux de preuves

Réseaux de preuves pour MLL

Cours 12: Axiomatisation catégorique de la logique linéaire

Rappels

Cours 13: Théorème de Friedman: correction

Nouveau professeur pour cette seconde partie : Michele Pagani

Cours 14: Théorème de Friedman: complétude

Friedman:

Cours 15: PCF

Expressivité

Cours 16: PCF: Logical Relations to show Adequacy

Rappels

Cours 17: Fully abstract models

Th: Let $M$ be a PCF program, then \[⟦M⟧ = n \text{ iff } M ⟶^\ast \underline{n}\]

Cours 18: Catégorie des Espaces Cohérents: Modèle de PCF

cf. Linear Logic, Girard (1987)

Cours 19: Probabilistic Coherence Spaces and the $PCoh$ category

Caractérisation des espaces cohérents

Cours 20: Probabilistic Coherence Spaces and Linear Logic

In Probabilistic Coherence Spaces, $𝒫(𝒜)$ is closed under $\sup$

Cours 1: Algèbre initiale

Algèbre intiale (pour un foncteur)

Cours 2: cohérence de $PA_2$, Girard et Takeuti

Imprédicativité:

Cours 3: Candidats de réductibilité de Tait

Comment pallier le problème de définition circulaire des candidats de réductibilité $Red(∀X. A)$ ?

Cours 4: Normalisation du système F

Expression, dans Peano, du fait que $Red_ρ(A)$ est un candidat de réductibilité:

Cours 5: Sémantique dénotationnelle

Introduction

Cours 6: Domaines de Scott

Dans une Catégorie Cartésienne Close (CCC)

Cours 7: Espace cohérents

Espaces cohérents

Cours 8: La catégories des espaces cohérents est monoïdale close

Catégories $Coh$ et $Stab$

Cours 9: Logique linéaire

Rappel: Le bang $!: Stab ⟶ Coh$ est l’adjoint à gauche du foncteur d’oubli $𝒰: Coh ⟶ Stab$:

Cours 10: Traduction de Girard et Substitution explicite

Traduction de Girard

Cours 11: Réseaux de preuves

Réseaux de preuves pour MLL

Cours 12: Axiomatisation catégorique de la logique linéaire

Rappels

Cours 13: Théorème de Friedman: correction

Nouveau professeur pour cette seconde partie : Michele Pagani

Cours 14: Théorème de Friedman: complétude

Friedman:

Cours 15: PCF

Expressivité

Cours 16: PCF: Logical Relations to show Adequacy

Rappels

Cours 17: Fully abstract models

Th: Let $M$ be a PCF program, then \[⟦M⟧ = n \text{ iff } M ⟶^\ast \underline{n}\]

Cours 18: Catégorie des Espaces Cohérents: Modèle de PCF

cf. Linear Logic, Girard (1987)

Cours 19: Probabilistic Coherence Spaces and the $PCoh$ category

Caractérisation des espaces cohérents

Cours 20: Probabilistic Coherence Spaces and Linear Logic

In Probabilistic Coherence Spaces, $𝒫(𝒜)$ is closed under $\sup$

TD: La catégorie $PCoh$

1. Le produit monoïdal $\otimes$ de $PCoh$

Cours 1: Algèbre initiale

Algèbre intiale (pour un foncteur)

TD: La catégorie $PCoh$

1. Le produit monoïdal $\otimes$ de $PCoh$

Cours 1: Algèbre initiale

Algèbre intiale (pour un foncteur)

Cours 2: cohérence de $PA_2$, Girard et Takeuti

Imprédicativité:

Cours 3: Candidats de réductibilité de Tait

Comment pallier le problème de définition circulaire des candidats de réductibilité $Red(∀X. A)$ ?

Cours 4: Normalisation du système F

Expression, dans Peano, du fait que $Red_ρ(A)$ est un candidat de réductibilité:

Cours 5: Sémantique dénotationnelle

Introduction

Cours 6: Domaines de Scott

Dans une Catégorie Cartésienne Close (CCC)

Cours 7: Espace cohérents

Espaces cohérents

Cours 8: La catégories des espaces cohérents est monoïdale close

Catégories $Coh$ et $Stab$

Cours 9: Logique linéaire

Rappel: Le bang $!: Stab ⟶ Coh$ est l’adjoint à gauche du foncteur d’oubli $𝒰: Coh ⟶ Stab$:

Cours 10: Traduction de Girard et Substitution explicite

Traduction de Girard

Cours 11: Réseaux de preuves

Réseaux de preuves pour MLL

Cours 12: Axiomatisation catégorique de la logique linéaire

Rappels

Cours 13: Théorème de Friedman: correction

Nouveau professeur pour cette seconde partie : Michele Pagani

Cours 14: Théorème de Friedman: complétude

Friedman:

Cours 15: PCF

Expressivité

Cours 16: PCF: Logical Relations to show Adequacy

Rappels

Cours 17: Fully abstract models

Th: Let $M$ be a PCF program, then \[⟦M⟧ = n \text{ iff } M ⟶^\ast \underline{n}\]

Cours 18: Catégorie des Espaces Cohérents: Modèle de PCF

cf. Linear Logic, Girard (1987)

Cours 19: Probabilistic Coherence Spaces and the $PCoh$ category

Caractérisation des espaces cohérents

Cours 20: Probabilistic Coherence Spaces and Linear Logic

In Probabilistic Coherence Spaces, $𝒫(𝒜)$ is closed under $\sup$

TD: La catégorie $PCoh$

1. Le produit monoïdal $\otimes$ de $PCoh$

TD: La catégorie $PCoh$

1. Le produit monoïdal $\otimes$ de $PCoh$

TD: La catégorie $PCoh$

1. Le produit monoïdal $\otimes$ de $PCoh$

Projet de Programmation : Compilateur C–

Méthode utilisée pour compiler les exceptions

TD8 : DCPO, Topologie de Scott

EX 1

DM : Programmation 1

DM Programmation I

DM : Programmation 1

DM Programmation I

Cours 1 : Introduction aux langages de programmation, Représentation des entiers

TD2 : Assembleur

TD2 de Programmation 1

TD4 : Pointeurs, Copies profondes

3

TD5 : Modes d’appel, Portée

1. Portée

TD6 : Sémantique

Simon Halfon :

Cours 2 : Architecture, Console, Assembleur

TD1 : Entiers, flottants, encodage

TD 1

Cours 3 : Structures de données en C, Types VS Classes de stockage, Arithmétique des pointeurs

Structures de données

Cours 4 : Modes d’appels, Portée lexicale/ Liaison dynamique

Modes d’appels

Cours 5 : Sémantique

Sémantique

DM : Programmation 1

DM Programmation I

Cours 7 : PCF

Plotkin et le PCF

TD12 : flux d’information sûrs

TD : flux d’information sûrs

TD7 : Adéquation, Théorèmes du point fixe, DCPO

1.

TD8 : DCPO, Topologie de Scott

EX 1

TD9 : Sémantique

EX 1

TD7 : Adéquation, Théorèmes du point fixe, DCPO

1.

Projet de Programmation : Compilateur C–

Méthode utilisée pour compiler les exceptions

Cours 1 : Introduction aux langages de programmation, Représentation des entiers

TD12 : flux d’information sûrs

TD : flux d’information sûrs

TD2 : Assembleur

TD2 de Programmation 1

TD4 : Pointeurs, Copies profondes

3

TD5 : Modes d’appel, Portée

1. Portée

TD6 : Sémantique

Simon Halfon :

TD7 : Adéquation, Théorèmes du point fixe, DCPO

1.

TD8 : DCPO, Topologie de Scott

EX 1

TD9 : Sémantique

EX 1

Cours 2 : Architecture, Console, Assembleur

TD1 : Entiers, flottants, encodage

TD 1

Cours 3 : Structures de données en C, Types VS Classes de stockage, Arithmétique des pointeurs

Structures de données

Cours 4 : Modes d’appels, Portée lexicale/ Liaison dynamique

Modes d’appels

Cours 5 : Sémantique

Sémantique

Structures de données

Projet de Programmation : Compilateur C–

Méthode utilisée pour compiler les exceptions

TD2 : Assembleur

TD2 de Programmation 1

Cours 2 : Architecture, Console, Assembleur

Cours 3 : Structures de données en C, Types VS Classes de stockage, Arithmétique des pointeurs

Structures de données

Projet de Programmation : Compilateur C–

Méthode utilisée pour compiler les exceptions

Cours 2 : Architecture, Console, Assembleur

TD4 : Pointeurs, Copies profondes

3

Cours 1 : Introduction aux langages de programmation, Représentation des entiers

Cours 2 : Architecture, Console, Assembleur

Cours 3 : Structures de données en C, Types VS Classes de stockage, Arithmétique des pointeurs

Structures de données

Cours 4 : Modes d’appels, Portée lexicale/ Liaison dynamique

Modes d’appels

Cours 5 : Sémantique

Sémantique

Cours 7 : PCF

Plotkin et le PCF

Programmation 2 : Modules

Etienne Lozes

Cours 1 : Introduction aux langages de programmation, Représentation des entiers

Cours 2 : Architecture, Console, Assembleur

Cours 3 : Structures de données en C, Types VS Classes de stockage, Arithmétique des pointeurs

Structures de données

Cours 4 : Modes d’appels, Portée lexicale/ Liaison dynamique

Modes d’appels

Cours 5 : Sémantique

Sémantique

Cours 7 : PCF

Plotkin et le PCF

Programmation 2 : Modules

Etienne Lozes

TP Programmation

DM : Programmation 1

DM Programmation I

Cours 7 : PCF

Plotkin et le PCF

Cours 1 : Introduction aux langages de programmation, Représentation des entiers

TD2 : Assembleur

TD2 de Programmation 1

TD4 : Pointeurs, Copies profondes

3

TD5 : Modes d’appel, Portée

1. Portée

TD6 : Sémantique

Simon Halfon :

Modes d’appels

Cours 2 : Architecture, Console, Assembleur

Projet de Programmation : Compilateur C–

Méthode utilisée pour compiler les exceptions

Cours 1 : Introduction aux langages de programmation, Représentation des entiers

TD12 : flux d’information sûrs

TD : flux d’information sûrs

TD2 : Assembleur

TD2 de Programmation 1

TD4 : Pointeurs, Copies profondes

3

TD5 : Modes d’appel, Portée

1. Portée

TD6 : Sémantique

Simon Halfon :

TD7 : Adéquation, Théorèmes du point fixe, DCPO

1.

TD8 : DCPO, Topologie de Scott

EX 1

TD9 : Sémantique

EX 1

TD1 : Entiers, flottants, encodage

TD 1

Cours 3 : Structures de données en C, Types VS Classes de stockage, Arithmétique des pointeurs

Structures de données

DM : Programmation 1

DM Programmation I

Programmation 2 : Modules

Etienne Lozes

TP Programmation

Cours 3 : Structures de données en C, Types VS Classes de stockage, Arithmétique des pointeurs

Structures de données

TD6 : Sémantique

Simon Halfon :

TD7 : Adéquation, Théorèmes du point fixe, DCPO

1.

TD8 : DCPO, Topologie de Scott

EX 1

TD9 : Sémantique

EX 1

Cours 5 : Sémantique

Sémantique

DM : Programmation 1

DM Programmation I

Cours 7 : PCF

Plotkin et le PCF

Practical session 5: Learning theory and PAC bounds

Exercises 6: Learning theory and PAC bounds

Practical session 5: Learning theory and PAC bounds

TP6b: Régression linéaire & logistique, Analyse discriminante linéaire & quadratique

Practical session 4: Convex optimization

TP4: Convex optimization

Practical session 4: Convex optimization

Practical session 10: LASSO Regression

TD 12: Révisions

Practical session 12: Summary

TP 11: PCA et $K$-Means

Practical session 11: PCA and K-Means

Exercises 5: Learning theory and PAC bounds

Practical session 5: Learning theory and PAC bounds

TP 11: PCA et $K$-Means

Practical session 11: PCA and K-Means

Practical session 10: LASSO Regression

TP6b: Régression linéaire & logistique, Analyse discriminante linéaire & quadratique

Practical session 4: Convex optimization

TP4: Convex optimization

Practical session 4: Convex optimization

Lecture 6: PAC-Learning

Probably Approximately Correct

Lecture 7: Maximum likelihood

Maximum likelihood

TP 11: PCA et $K$-Means

Practical session 11: PCA and K-Means

TP6b: Régression linéaire & logistique, Analyse discriminante linéaire & quadratique

Practical session 4: Convex optimization

Exercises 5: Learning theory and PAC bounds

Practical session 5: Learning theory and PAC bounds

Exercises 6: Learning theory and PAC bounds

Practical session 5: Learning theory and PAC bounds

TD 12: Révisions

Practical session 12: Summary

TD 12: Révisions

Practical session 12: Summary

Exercises: Convex optimization

Practical session 3: linear regression

Exercises 5: Learning theory and PAC bounds

Practical session 5: Learning theory and PAC bounds

Exercises 6: Learning theory and PAC bounds

Practical session 5: Learning theory and PAC bounds

Practical session 10: LASSO Regression

TP 11: PCA et $K$-Means

Practical session 11: PCA and K-Means

TP1: Linear Regression

Practical session 1: linear regression

10 minute read

Petit tutoriel à l’intention de mes amis pour créer leur site web.

Younesse Kaddar

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