Other

What we didn't (much) cover

  • Hypothesis testing
    • p-values: the p-value is the probability of obtaining a test statistic at least as extreme as the one that was actually observed, assuming that the null hypothesis is true
    • Confidence intervals, standard error estimates
  • Randomized search for MLE/MAP
    • Gibbs Sampling /MCMC (alternative to EM)
  • Metric learning
    • {$x^\top A x$}
  • Domain adaptation
    • adapt model from one distribution {$p(x,y)$} to another
  • Structured data
    • {$x$} can be a graph: use graph kernels, graph Laplacians
    • {$y$} can be a structure (e.g. a parse tree)
  • Meta-learning (auto-ML)
    • Search over hyper-parameters, network architectures, ...

We only touched on

  • Multitask learning
    • simultaneously predict multiple {$y$}s from the same features. (e.g. CCA)
  • Reinforcement learning
    • Choose sequence of actions (or policy) to maximize expected reward
    • Markov Decision Processes (MDP, POMDP)
  • Time series in deep learning
    • GNNs, LSTM's generalize HMMs

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