

lecture
Gaussian Process Basics
as author at Gaussian Processes in Practice Workshop, Bletchley Park 2006,
43506 views




lecture
Lecture 1: Introduction to Information Theory
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
23038 views



tutorial
Information Theory
as author at Machine Learning Summer School (MLSS), Cambridge 2009,
14781 views




lecture
Lecture 2: Entropy and Data Compression (I): Introduction to Compression, Information Theory and Entropy
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
5493 views



event
Course on Information Theory, Pattern Recognition, and Neural Networks
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
together with:
David MacKay (University of Cambridge) (produced by),




lecture
Lecture 3: Entropy and Data Compression (II): Shannon's Source Coding Theorem and the Bent Coin Lottery
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
2831 views



lecture
Lecture 10: An Introduction To Bayesian Inference (II): Inference Of Parameters And Models
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
2675 views




lecture
Lecture 4: Entropy and Data Compression (III): Shannon's Source Coding Theorem, Symbol Codes
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
2596 views



lecture
Lecture 12: Approximating Probability Distributions (II): Monte Carlo Methods (I): Importance Sampling, Rejection Sampling, Gibbs Sampling, Metropolis Method
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
2556 views




lecture
Lecture 5: Entropy and Data Compression (IV): Shannon's Source Coding Theorem, Symbol Codes and Arithmetic Coding
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
2045 views



lecture
Lecture 15: Data Modelling With Neural Networks (I): Feedforward Networks: The Capacity Of A Single Neuron, Learning As Inference
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
2004 views




lecture
Lecture 6: Noisy Channel Coding (I): Inference and Information Measures for Noisy Channels
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
1949 views



lecture
Lecture 9: A Noisy Channel Coding Gem, And An Introduction To Bayesian Inference (I)
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
1869 views




lecture
Lecture 14: Approximating Probability Distributions (IV): Variational Methods
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
1676 views



lecture
Lecture 13: Approximating Probability Distributions (III): Monte Carlo Methods (II): Slice Sampling, Hybrid Monte Carlo, Overrelaxation, Exact Sampling
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
1590 views




lecture
Lecture 7: Noisy Channel Coding (II): The Capacity of a Noisy Channel
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
1564 views



lecture
Lecture 11: Approximating Probability Distributions (I): Clustering As An Example Inference Problem
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
1538 views




lecture
Lecture 8: Noisy Channel Coding (III): The NoisyChannel Coding Theorem
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
1485 views



lecture
Lecture 16: Data Modelling With Neural Networks (II): ContentAddressable Memories And StateOfTheArt ErrorCorrecting Codes
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
1253 views

