IMPORTANT NOTICE: the current webpage for the reading group can be found hereLink opens in a new window.

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Suggested topics and papers:

• Lloyd et al. (2014) Automatic construction and natural-language description of nonparametric regression models.Link opens in a new window More about the Automatic Statistician can be found hereLink opens in a new window.
• Wang et al. (2015) Regularised principal component analysis for spatial data.Link opens in a new window
• Singh et al. (2008) A unified view of matrix factorisation models.Link opens in a new window
• Dwork et al. (2015) The reusable holdout: preserving validity in adaptive data analysis.Link opens in a new window
• Particle filtering. Resources include: Murphy (2012) Machine Learning: A Probabilistic Perspective chapter 23.5 (available from the university library, or see Jim Skinner for other forms of access).
• Combrisson et al. (2015) Exceeding chance level by chance: the caveat of theoretical chance levels in brain signal classification and statistical assessment of decoding accuracyLink opens in a new window

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Previously discussed topics and papers:

• Korb et al. (1999) Bayesian poker.Link opens in a new window
• Wilson et al. (2015) Kernel interpolation for scalable structured Gaussian processes (KISS-GP).Link opens in a new window
• Morey et al. (2015) The fallacy of placing confidence in confidence intervals.Link opens in a new window
• VC dimension. Resources include: Vapnik (1999) An overview of statistical learning theoryLink opens in a new window.
• Dropout for neural networks. Resources include: Srivastava (2014) Dropout: a simple way to prevent neural networks from overfittingLink opens in a new window.
• Heller et al. (2005) Randomized algorithms for fast bayesian hierarchical clustering.Link opens in a new window
• Speech recognition. Resources include: Deng et al. (2013) Machine learning paradigms for speech recognision: an overviewLink opens in a new window.
• Latent Dirichlet allocation. Resources include: Murphy (2012) Machine Learning: A Probabilistic Perspective chapter 27.3 (available from the university library, or see Jim Skinner for other forms of access).
• Gaussian process regression derivations. Resources include: Williams et al. (1996) Gaussian processes for regressionLink opens in a new window, Zhu (1997) Gaussian regression and optimal finite dimensional linear modelsLink opens in a new window, and the Gaussian processes website tutorial sectionLink opens in a new window.
• GP-LVM. Resources include: Lawrence (2005) Probabilistic non-linear principal component analysis with Gaussian process latent variable modelsLink opens in a new window, and Lawrence (2003) Gaussian process latent variable models for visualisation of high dimensional dataLink opens in a new window.
• Boosted treesLink opens in a new window. Further information can be found hereLink opens in a new window.
• Faraway (2014) Does data splitting improve prediction?Link opens in a new window
• Recommendation engines. Resources include: Leskovec et al. (2014) Mining of Massive Datasets chapter 9 (available hereLink opens in a new window).
• Lo et al. (2015) Why significant variables aren't automatically good predictors.Link opens in a new window
• Naive Bayes. Resources include: Murphy (2012) Machine Learning: A Probabilistic Perspective chapters 3.5 and 10.2.1 (available from the university library, or see Jim Skinner for other forms of access).
• Latent linear models. Resources include: Murphy (2012) Machine Learning: A Probabilistic Perspective chapters 12.1 and 12.3 (available from the university library, or see Jim Skinner for other forms of access).
• Kernels and SVMs. Resources include: Murphy (2012) Machine Learning: A Probabilistic Perspective chapter 14 (available from the university library, or see Jim Skinner for other forms of access).
• Guest speaker: Mauricio ÁlvarezLink opens in a new window. NB: different location - room B3.02.
  Gaussian processes in Applied Neuroscience: a case study in Deep Brain Stimulation.
  

  Deep brain stimulation (DBS) is a treatment for movement disorders, such as Parkinson's disease, dystonia, or essential tremor. It usually consists of the implantation of a stimulator in the infraclavicular region connected to an electrode lead that is placed in a target structure in the basal ganglia, particularly in the subthalamic nucleus, or the thalamus. The stimulator delivers electric pulses of a specific frequency, amplitude and pulse-width to the target via the electrode, which results in symptom improvement. In this talk, I will review different topics about how we have been using recent developments in Gaussian processes to tackle different inference problems that appear when dealing with the analysis of the process of DBS. In particular, I will talk about generalized Wishart processes for diffusion tensor estimation, and latent force models for simulating electrical potential fields.

• Ciresan et al. (2012) Multi-column deep neural networks for image classification.Link opens in a new window
• Genton (2001) Classes of kernels for machine learning: a statistics perspective.Link opens in a new window
• Reinforcement learning. Resources include: Sutton et al. (2012) Reinforcement Learning: An Introduction chapter 1 (available hereLink opens in a new window).
• Heller et al. (2005) Bayesian hierarchical clustering.Link opens in a new window
• Dirichlet processes. Resources include: Murphy (2012) Machine Learning: A Probabilistic Perspective chapter 25.2 (available from the university library, or see Jim Skinner for other forms of access).
• Sohl-Dickstein (2015) Deep unsupervised learning using nonequilibrium thermodynamics.Link opens in a new window
• Recurrent neural networks. Resources include: Goodfellow et al. Deep Learning (unfinished) chapter 10 (available hereLink opens in a new window) and Karpathy's blogpost on the topic (available hereLink opens in a new window).
• Kulis (2012) Revisiting k-means: new algorithms via bayesian nonparametrics.Link opens in a new window
  
• Ghahramani (2001) An introduction to hidden Markov models and Bayesian networks.Link opens in a new window
• Video lecture: The next generation of neural networks - Geoff Hinton.Link opens in a new window
• Stanley et al. (2005) Evolving neural network agents in the NERO video game.Link opens in a new window
• Gaussian mixture models. Resources include: Bishop (2006) Pattern Recognition and Machine Learning chapter 9.2 (available in both the Complexity Centre and university libraries).
• Domingos (2012) A few useful things to know about machine learning.Link opens in a new window
• Mikolov et al. (2013) Efficient estimation of word representations in vector space.Link opens in a new window Tool resources can be found hereLink opens in a new window. A summarising presentation can be found hereLink opens in a new window.
• Schmidt et al. (2009) Distilling free-form natural laws from experimental data.Link opens in a new window
• Multiple testing. Resources include: Bland et al. (1995) Multiple significance tests: the Bonferroni methodLink opens in a new window, and Scott et al. (2003) An exploration of aspects of Bayesian multiple testingLink opens in a new window.
• Variational inference. Resources include: Bishop (2006) Pattern Recognition and Machine Learning chapter 10.1 (available in both the Complexity Centre and university libraries), and Murphy (2012) Machine Learning: A Probabilistic Perspective chapters 21.1, 21.2, and 21.5 (available from the university library, or see Jim Skinner for other forms of access).
• Previous topics and papers read during 2014/15.Link opens in a new window