Neural Networks
Theoretical Neuroscience and Deep Learning Theory
Deep Learning for Machine Vision
Extracting and Composing Robust Features with Denoising Autoencoders
Classification using Discriminative Restricted Boltzmann Machines
Adversarial Examples
Information Theoretic Kernel Integration
Bayesian Inference
Monte Carlo Simulation for Statistical Inference, Model Selection and Decision Making
Bayesian Probabilistic Matrix Factorization using Markov Chain Monte Carlo