Sergey Nikolenko

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nikolenko (in Russian)

Teaching activities

Probabilistic Learning

The course materials (all slides and lecture notes are in Russian):

1. Introduction. Probability theory and Bayesian inference.

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2. Bayesian inference by brute force. Exact marginalization by integration.

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3. Bayesian inference and coding theory. Error correcting as MAP hypothesis search.

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4. General sum-product algorithm for marginalization. Laplace approximations.

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5. Monte–Carlo methods: sampling. Markov chains, MCMC, Metropolis–Hastings, slice sampling.

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6. Hidden Markov models. Introduction, main tasks, their solutions. Baum–Welch algorithm.

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7. Hidden Markov models II. HMM with continuous observables, autoregressive models, HMM comparison, MMI and MDI criteria.

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8. Bayesian approach to neural networks. Adapting learning speed. Learning neural networks as Bayesian inference. Gaussian processes and splines.

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9. Hopfield networks.

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1. David J. C. MacKay. Information Theory, Inference, and Learning Algorithms. Cambridge University Press, 2003.