Computational Perception Group
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Reading list
Reading and online material related to the Group's research programme:
General Statistics and Machine learning texts
Bayesian Reasoning and Machine Learning, David Barber
Machine Learning: A Probabilistic Perspective, Kevin Murphy
Information Theory, Inference, and Learning Algorithms, David Mackay
Pattern Recognition and Machine Learning, Chris Bishop
Specific Statistics and Machine Learning texts
Gaussian Processes for Machine Learning, Carl Rasmussen and Chris Williams
Convex Optimization, Stephen Boyd and Lieven Vandenberghe
Video lectures on topics in Statistics and Machine Learning
Course on Information Theory, Pattern Recognition, and Neural Networks
Gaussian Process Basics
Learning with Gaussian Processes
Graphical Models
Why Bayesian nonparametrics?
Modern Bayesian Nonparametrics
Dirichlet Processes, Chinese Restaurant Processes, and all that
Expectation Propagation
Deep Belief Networks
Particle Filtering
Introduction to Machine Learning
Theoretical Neuroscience
Theoretical Neuroscience: Computational and Mathematical Modeling of Neural Systems, Peter Dayan and Larry Abbott
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Hearing
Auditory Neuroscience: Making Sense of Sound
Hearing, Brian Moore
Auditory Scene Analysis, Albert Bregman
Signal Processing
Discrete-time Signal Processing
Engineering Department
CBL Home
Biological learning
Machine learning
Faculty
Ghahramani
Hennequin
Hernández-Lobato
Lengyel
Rasmussen
Turner
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Wolpert
Seymour
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