Dimensionality reduction Wherein I teach myself, amongst other things, feature selection, how a sparse PCA works, and decide where to file multidimensional scaling 2015-03-22 – 2020-09-11

Simulation based inference a.k.a. Approximate Bayesian Computation or Likelihood-Free-Inference 2020-08-25

(Approximate) matrix factorisation 2014-08-05 – 2020-07-03

Learning of manifolds Also topological data analysis; other hip names to follow 2014-08-19 – 2020-06-23

Likelihood free inference 2020-04-22

Learning summary statistics 2020-04-22

Learnable indexes and hashes 2018-01-12 – 2020-02-18

Non-negative matrix factorisation 2019-10-14

Fourier interpolation 2019-06-19

Entity embeddings 2017-04-01

Randomised regression 2017-01-13 – 2017-02-22

Random embeddings and hashing 2016-12-05 – 2017-02-13

Clustering 2015-05-22 – 2016-06-07

Indirect inference 2014-12-23 – 2015-12-15