nhaliday + acm + course   47

Stat 260/CS 294: Bayesian Modeling and Inference
- Priors (conjugate, noninformative, reference)
- Hierarchical models, spatial models, longitudinal models, dynamic models, survival models
- Testing
- Model choice
- Inference (importance sampling, MCMC, sequential Monte Carlo)
- Nonparametric models (Dirichlet processes, Gaussian processes, neutral-to-the-right processes, completely random measures)
- Decision theory and frequentist perspectives (complete class theorems, consistency, empirical Bayes)
- Experimental design
unit  course  berkeley  expert  michael-jordan  machine-learning  acm  bayesian  probability  stats  lecture-notes  priors-posteriors  markov  monte-carlo  frequentist  latent-variables  decision-theory  expert-experience  confidence  sampling 
july 2017 by nhaliday
CS 731 Advanced Artificial Intelligence - Spring 2011
- statistical machine learning
- sparsity in regression
- graphical models
- exponential families
- variational methods
- dimensionality reduction, eg, PCA
- Bayesian nonparametrics
- compressive sensing, matrix completion, and Johnson-Lindenstrauss
course  lecture-notes  yoga  acm  stats  machine-learning  graphical-models  graphs  model-class  bayesian  learning-theory  sparsity  embeddings  markov  monte-carlo  norms  unit  nonparametric  compressed-sensing  matrix-factorization  features 
january 2017 by nhaliday
CS229T/STATS231: Statistical Learning Theory
Course by Percy Liang covers a mix of statistics, computational learning theory, and some online learning. Also surveys the state-of-the-art in theoretical understanding of deep learning (not much to cover unfortunately).
yoga  stanford  course  machine-learning  stats  👳  lecture-notes  acm  kernels  learning-theory  deep-learning  frontier  init  ground-up  unit  dimensionality  vc-dimension  entropy-like  extrema  moments  online-learning  bandits  p:***  explore-exploit  advanced 
june 2016 by nhaliday

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