This tutorial addresses the fundamentals of statistical models and neural networks, and focus on a series of advanced Bayesian models and deep models including hierarchical Dirichlet process , Chinese restaurant process (Blei et al., 2010) , hierarchical Pitman-Yor process (Teh, 2006) , Indian buffet process (Ghahramani and Griffiths, 2005) , recurrent neural network (Mikolov et al., 2010; Van Den Oord et al., 2016) , long short-term memory (Hochreiter and Schmidhuber, 1997; , sequence-to-sequence model (Sutskever et al., 2014), variational auto-encoder (Kingma and Welling, 2014) , generative adversarial network (Goodfellow et al., 2014) , attention mechanism (Chorowski et al., 2015; Seo et al., 2016) , memory-augmented neural network (Graves et al., 2014; Graves et al., 2014) , stochastic neural network <cite>Miao et al., 2016)</cite> , predictive state neural network (Downey et al., 2017) , policy gradient (Yu et al., 2017) and reinforcement learning (Mnih et al., 2015) .