Jakob Foerster

Jakob Foerster Faculty Talk Agency in the Era of Learning Systems

Jakob Foerster

Lecture 2 Entropy and Data Compression (I) Introduction to Compression, Inf.Theory and Entropy

Jakob Foerster

Lecture 7 Noisy Channel Coding (II) The Capacity of a Noisy Channel

Jakob Foerster

Lecture 14 Approximating Probability Distributions (IV) Variational Methods

Jakob Foerster

Lecture 4 Entropy and Data Compression (III) Shannon's Source Coding Theorem, Symbol Codes

Jakob Foerster

Lecture 11 Approximating Probability Distributions (I) Clustering As An Example Inference Problem

Jakob Foerster

Lecture 16 Data Modelling With Neural Networks (II) Content-Addressable Memories And State

Jakob Foerster

Lecture 8 Noisy Channel Coding (III) The Noisy-Channel Coding Theorem

Jakob Foerster

Lecture 3 Entropy and Data Compression (II) Shannon's Source Coding Theorem, The Bent Coin Lottery

Jakob Foerster

Lecture 12 Approximating Probability Distributions (II) Monte Carlo Methods (I)

Jakob Foerster

Lecture 10 An Introduction To Bayesian Inference (II) Inference Of Parameters And Models

Jakob Foerster

Lecture 5 Entropy and Data Compression (IV) Shannon's Source Coding Theorem, Symbol Codes

Jakob Foerster

Lecture 15 Data Modelling With Neural Networks (I) Feedforward Networks The Capacity Of A Neuron

Jakob Foerster

Lecture 13 Approximating Probability Distributions (III) Monte Carlo Methods (II) Slice Sampling

Jakob Foerster

Lecture 9 A Noisy Channel Coding Gem, And An Introduction To Bayesian Inference (I)

Jakob Foerster

Lecture 1 Introduction to Information Theory

Jakob Foerster

Lecture 6 Noisy Channel Coding (I) Inference and Information Measures for Noisy Channels

Jakob Foerster