Reading list

Reading and online material related to my 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.

Hearing

• Auditory Neuroscience: Making Sense of Sound
• Hearing, Brian Moore
• Auditory Scene Analysis, Albert Bregman

Signal Processing

• Discrete-time Signal Processing

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