01:57:58

MLSS 2012: B. Schölkopf - Session 3: Kernel Methods

53:32

MLSS 2012: G. Lugosi - Session 3: Concentration Inequalities in Machine Learning (Part 1)

01:57:21

MLSS 2012: P. Orbanz - Session 1: Bayesian Nonparametrics

48:36

MLSS 2012: G. Lugosi - Session 2: Concentration Inequalities in Machine Learning (Part 2)

01:07:34

MLSS 2012: N. Lawrence - Session 4: Introduction to Learning with Probabilities (Part 1)

55:46

MLSS 2012: R. Vanderbei - Session 3: Interior Point Methods and Nonlinear Optimisation (Part 1)

57:24

MLSS 2012: D. Gorur - Session 2: Dirichlet Processes: practical course

01:56:57

MLSS 2012: N. Lawrence - Session 3: Nonlinear Probabilistic Dimensionality Reduction

52:41

MLSS 2012: J. Cunningham - Gaussian Processes for Machine Learning (Part 2)

01:11:06

MLSS 2012: M. Giroloami - Session 3: Diffusions and Geodesic Flows on Manifolds... (Part 1)

49:12

MLSS 2012: J. Cunningham - Gaussian Processes for Machine Learning (Part 1)

01:06:11

MLSS 2012: R. Vanderbei - Session 1: Linear Optimisation, Duality, simplex, methods (Part 1)

01:07:17

MLSS 2012: Z. Ghahramani - Lecture 3: Graph based semi-supervised learning (Part 1)

50:10

MLSS 2012: G. Lugosi - Session 1: Concentration Inequalities in Machine Learning (Part 2)

32:49

MLSS 2012: G. Lugosi - Session 3: Concentration Inequalities in Machine Learning (Part 2)

01:00:50

MLSS 2012: P. Orbanz - Session 2: Bayesian Nonparametrics (Part 1)

52:59

MLSS 2012: P. Orbanz - Session 2: Bayesian Nonparametrics (Part 2)

01:09:27

MLSS 2012: D. Gorur - Session 1: Kingman's Coalescent for Hierarchical Representations (Part 1)

51:05

MLSS 2012: G. Lugosi - Session 2: Concentration Inequalities in Machine Learning (Part 1)

20:06

MLSS 2012: Z. Ghahramani - Lecture 3: Graph based semi-supervised learning (Part 2)

47:56

MLSS 2012: G. Lugosi - Session 1: Concentration Inequalities in Machine Learning (Part 1)

42:16

MLSS 2012: R. Vanderbei - Session 3: Interior Point Methods and Nonlinear Optimisation (Part 2)

01:06:46

MLSS 2012: N. Lawrence - Session 4: Introduction to Learning with Probabilities (Part 2)

49:24

MLSS 2012: M. Giroloami - Session 3: Diffusions and Geodesic Flows on Manifolds... (Part 2)

41:36

MLSS 2012: D. Gorur - Session 1: Kingman's Coalescent for Hierarchical Representations (Part 2)

01:08:24

MLSS 2012: R. Vanderbei - Session 2: Linear Optimisation: Methods and Examples (Part 1)

40:06

MLSS 2012: R. Vanderbei - Session 2: Linear Optimisation: Methods and Examples (Part 2)

47:04

MLSS 2012: R. Vanderbei - Session 1: Linear Optimisation, Duality, simplex, methods (Part 2)

44:26

MLSS 2012: F. Perez-Cruz - Channel coding with LDPC codes (Part 2)

51:10

MLSS 2012: F. Perez-Cruz - Channel coding with LDPC codes (Part 1)

01:10:21

MLSS 2012: M. Giroloami - Session 2: Diffusions and Geodesic Flows on Manifolds... (Part 1)

01:07:05

MLSS 2012: B. Schölkopf - Session 2: Kernel Methods (Part 1)

44:16

MLSS 2012: M. Giroloami - Session 2: Diffusions and Geodesic Flows on Manifolds... (Part 2)

34:13

MLSS 2012: B. Schölkopf - Session 2: Kernel Methods (Part 2)

01:58:23

MLSS 2012: B. Schölkopf - Introduction to Machine Learning / Kernel Methods

01:00:19

MLSS 2012: N. Lawrence - Spectral approaches to dimensionality reduction (Part 1)

01:16:27

MLSS 2012: M. Girolami - Diffusions and Geodesic flows in Manifolds... (Part 1)

01:10:20

MLSS 2012: N. Lawrence - Session 1: Motivation and Linear Models (Part 1)

57:45

MLSS 2012: N. Lawrence - Spectral approaches to dimensionality reduction (Part 2)

46:49

MLSS 2012: N. Lawrence - Session 1: Motivation and Linear Models (Part 2)

31:55

MLSS 2012: M. Girolami - Diffusions and Geodesic flows in Manifolds... (Part 2)

01:59:41

MLSS / AISTATS 2012: Bayesian Modelling

55:02

AISTATS 2012: Hierarchical Latent Dictionaries for Models of Brain Activation

46:03

AISTATS/MLSS 2012: Nonparametric Bayesian Modelling / Graphical Models, Part B

19:20

AISTATS 2012: Efficient Gaussian Process Inference for Short-Scale Spatio-Temporal Modeling

01:03:33

AISTATS/MLSS 2012: Nonparametric Bayesian Modelling / Graphical Models (Part A)

10:05

AISTATS 2012: Regression for sets of polynomial equations

53:17

AISTATS/MLSS 2012: Probabilistic decision-making, data analysis, and discovery in astronomy, I

45:40

AISTATS/MLSS 2012: Probabilistic decision-making, data analysis, and discovery in astronomy, II

19:40

AISTATS 2012: Adaptive MCMC with Bayesian Optimization

25:02

AISTATS 2012: Evaluation of marginal likelihoods via the density of states

53:51

AISTATS 2012: Detection of correlations in high dimension

23:38

AISTAS 2012: Fast Learning Rate of Multiple Kernel Learning...

25:25

AISTATS 2012: Data dependent kernels in nearly-linear time

25:23

AISTATS 2012: Factorized Asymptotic Bayesian Inference for Mixture Modeling

01:10:00

AISTATS 2012: Alpha, Betti and the Megaparsec Universe: Topology of the Cosmic Web

30:50

AISTATS 2012: Learning Fourier Sparse Set Functions

21:36

AISTATS 2012: Classifier Cascade for Minimizing Feature Evaluation Cost

20:18

AISTATS 2012: A Differentially Private Stochastic Gradient Descent Algorithm for Multiparty...

19:16

AISTATS 2012: High-dimensional Sparse Inverse Covariance Estimation using Greedy Methods