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lecture
Lecture 16: Data Modelling With Neural Networks (II): Content-Addressable Memories And State-Of-The-Art Error-Correcting Codes
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lecture
Lecture 15: Data Modelling With Neural Networks (I): Feedforward Networks: The Capacity Of A Single Neuron, Learning As Inference
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lecture
Lecture 14: Approximating Probability Distributions (IV): Variational Methods
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lecture
Lecture 13: Approximating Probability Distributions (III): Monte Carlo Methods (II): Slice Sampling, Hybrid Monte Carlo, Over-relaxation, Exact Sampling
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13682 views
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lecture
Lecture 12: Approximating Probability Distributions (II): Monte Carlo Methods (I): Importance Sampling, Rejection Sampling, Gibbs Sampling, Metropolis Method
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Lecture 11: Approximating Probability Distributions (I): Clustering As An Example Inference Problem
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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,
20776 views
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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,
14557 views
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lecture
Lecture 8: Noisy Channel Coding (III): The Noisy-Channel Coding Theorem
as author at Course on Information Theory, Pattern Recognition, and Neural Networks,
14135 views
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Lecture 7: Noisy Channel Coding (II): The Capacity of a Noisy Channel
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13658 views
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lecture
Lecture 6: Noisy Channel Coding (I): Inference and Information Measures for Noisy Channels
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lecture
Lecture 5: Entropy and Data Compression (IV): Shannon's Source Coding Theorem, Symbol Codes and Arithmetic Coding
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17472 views
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lecture
Lecture 4: Entropy and Data Compression (III): Shannon's Source Coding Theorem, Symbol Codes
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19317 views
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lecture
Lecture 3: Entropy and Data Compression (II): Shannon's Source Coding Theorem and the Bent Coin Lottery
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21823 views
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lecture
Lecture 2: Entropy and Data Compression (I): Introduction to Compression, Information Theory and Entropy
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35554 views
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event
Course on Information Theory, Pattern Recognition, and Neural Networks
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together with:
David MacKay (University of Cambridge) (produced by),
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lecture
Lecture 1: Introduction to Information Theory
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tutorial
Information Theory
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lecture
Gaussian Process Basics
as author at Gaussian Processes in Practice Workshop, Bletchley Park 2006,
210521 views
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Springer,
