Bayesian Nonparametrics


  • Nonparametric Bayesian Methods (Larry A. Wasserman)
  • Fundamentals of Nonparametric Bayesian Inference (Subhashis Ghosal and Aad van der Vaart)
  • Gaussian Processes for Machine Learning (Carl Edward Rasmussen and Christopher K. I. Williams)

    • Deep Learning


  • A Selective Overview of Deep Learning (Jianqing Fan, Cong Ma, Yiqiao Zhong)
  • Deep Learning: A Bayesian Perspective (Nicholas Polson, Vadim Sokolov)
  • A Comprehensive Survey on Graph Neural Networks (Zonghan Wu et al.)
  • Approximation and estimation bounds for artificial neural networks (Andrew R. Barron)
  • Theoretical Issues in Deep Networks (Tomaso Poggio, Andrzej Banburski, and Qianli Liao)
  • Adaptive Approximation and Generalization of Deep Neural Network with Intrinsic Dimensionality (Ryumei Nakada and Masaaki Imaizumi)

    • Monte Carlo Methods


  • Monte Carlo Statistical Methods (Christian P. Robert and George Casella)
  • Monte Carlo Theory, Methods and Examples (Art Owen)
  • An Introduction to MCMC for Machine Learning (Christophe Andrieu, Nando de Freitas, Arnaud Doucet and Michael I. Jordan)

    • Optimization


  • Optimization Methods for Large-Scale Machine Learning (Léon Bottou, Frank E. Curtis, Jorge Nocedal)
  • Alternating Direction Method of Multipliers (Stephen P. Boyd)
  • Proximal Algorithm (Neal Parikh and Stephen Boyd)

    • Programming


  • R: Advanced R (Hadley Wickham); Efficient R programming (Colin Gillespie and Robin Lovelace); Rcpp for everyone (Masaki E. Tsuda)
  • C++: Armadillo; Eigen; GNU Sceintific Library
  • Python: scikit-learn; TensorFlow; PyTorch
  • More Progamming Skills

    • Neuroimaging Tools


  • FMRIB Software Library (FSL) (Course; R package: fslr)
  • Analysis of Functional NeuroImages (AFNI)
  • Statistical Parametric Mapping (SPM)
  • Advanced Normalization Tools (ANTs)
  • Visualization: Mango; MRIcron; BrainNet Viewer