mlss 2020
 01:35:30 
CAUSALITY, PART 1 - BERNHARD SCHÖLKOPF - MLSS 2020, TÜBINGEN

CAUSALITY, PART 1 - BERNHARD SCHÖLKOPF - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Causality, part 1 - Bernhard Scholkopf - MLSS 2020, Tubingen .
 01:28:26 
VIRTUAL MLSS 2020 (OPENING REMARKS)

VIRTUAL MLSS 2020 (OPENING REMARKS)

Table of Contents (powered by ) 0:00:00 [Opening remark- Virtual MLSS 2020] 0:00:21 Machine Learning .
 01:37:07 
DEEP LEARNING, PART 1 - YOSHUA BENGIO - MLSS 2020, TÜBINGEN

DEEP LEARNING, PART 1 - YOSHUA BENGIO - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Introduction 0:02:30 Deep Learning for AI 0:02:59 Neural Networks .
 01:40:01 
FAIRNESS, PART 1 - MORITZ HARDT - MLSS 2020, TÜBINGEN

FAIRNESS, PART 1 - MORITZ HARDT - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Fairness, part 1 - Moritz Hardt - MLSS 2020, Tübingen 0:02:16 .
 01:32:56 
QUANTUM MACHINE LEARNING - MARIA SCHULD - MLSS 2020, TÜBINGEN

QUANTUM MACHINE LEARNING - MARIA SCHULD - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Speaker Introduction 0:01:13 Quantum Machine Learning 0:03:27 .
 01:38:50 
OPTIMIZATION, PART 1 - FRANCIS BACH - MLSS 2020, TÜBINGEN

OPTIMIZATION, PART 1 - FRANCIS BACH - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 MLSS 0:01:57 Optimization for Large Scale Machine Learning .
 01:32:00 
KERNEL METHODS, PART 1 - ARTHUR GRETTON - MLSS 2020, TÜBINGEN

KERNEL METHODS, PART 1 - ARTHUR GRETTON - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Introduction 0:02:10 Representing and comparing probabilities with .
 01:38:26 
DEEP REINFORCEMENT LEARNING, PART 1 - DOINA PRECUP - MLSS 2020, TÜBINGEN

DEEP REINFORCEMENT LEARNING, PART 1 - DOINA PRECUP - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Speaker Introduction 0:01:22 Introduction to Reinforcement .
 01:53:01 
BAYESIAN INFERENCE, PART 2 - SHAKIR MOHAMED - MLSS 2020, TÜBINGEN

BAYESIAN INFERENCE, PART 2 - SHAKIR MOHAMED - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Kernel Quiz (by default, x, x' X, with A' a nonempty set 0:05:06 .
 01:38:21 
BAYESIAN PREDICTION WITH STREAMING DATA - SONIA PETRONE - MLSS 2020, TÜBINGEN

BAYESIAN PREDICTION WITH STREAMING DATA - SONIA PETRONE - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Bayesian Prediction with Streaming Data - Sonia Petrone - MLSS .
 01:39:26 
GAME THEORY IN MACHINE LEARNING, PART 1 - COSTANTINOS DASKALAKIS - MLSS

GAME THEORY IN MACHINE LEARNING, PART 1 - COSTANTINOS DASKALAKIS - MLSS

Table of Contents (powered by ) 0:00:00 Game Theory in Machine Learning, part 1 - Costantinos Daskalakis .
 01:31:25 
GEOMETRIC DEEP LEARNING - MICHAEL BRONSTEIN - MLSS 2020, TÜBINGEN

GEOMETRIC DEEP LEARNING - MICHAEL BRONSTEIN - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Speaker Introduction 0:02:04 Geometric Deep Learning - going .
 01:42:36 
OPTIMIZATION, PART 2 - FRANCIS BACH - MLSS 2020, TÜBINGEN

OPTIMIZATION, PART 2 - FRANCIS BACH - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Optimization for Large Scale Machine Learning 0:01:14 Stochastic .
 01:48:11 
META LEARNING, PART 2 - YEE WHYE TEH - MLSS 2020, TÜBINGEN

META LEARNING, PART 2 - YEE WHYE TEH - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Base Learner Architecture and Task Representation 0:00:37 .
 01:41:32 
MLSS INDO 2020 - LECTURE 1 - MACHINE LEARNING BASICS

MLSS INDO 2020 - LECTURE 1 - MACHINE LEARNING BASICS

The Machine Learning Summer School Indonesia (MLSS-Indo) is part of MLSS series ( ), which was started at Max Planck .
 01:28:06 
MACHINE LEARNING FOR HEALTHCARE, PART 1 - MIHAELA VAN DER SCHAAR -

MACHINE LEARNING FOR HEALTHCARE, PART 1 - MIHAELA VAN DER SCHAAR -

Table of Contents (powered by ) 0:00:00 Speaker Introduction 0:01:22 Machine learning for healthcare .
 01:32:11 
DEEP LEARNING, PART 2 - YOSHUA BENGIO - MLSS 2020, TÜBINGEN

DEEP LEARNING, PART 2 - YOSHUA BENGIO - MLSS 2020, TÜBINGEN

Table of Contents (powered by ) 0:00:00 Classifiers for model distributions 0:08:17 Generative adversarial .
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