Errata to the 1st printing (10 9 8 7 6 5 4 3 2 1 in ISBN number page) of:
  Ethem Alpaydin: "Introduction to Machine Learning", 3rd ed.
  MIT Press, 2014.
Miguel A. Carreira-Perpinan, 2015.

General comments:
- Many figures in the book have the wrong aspect ratio, and in some of them
  this affects the comprehension of the material. For example, fig. 7.2
  about k-means is too stretched vertically, so that the distances are
  distorted and some points appear to be assigned to the wrong cluster.
  In fig. 6.12, the Opdigits after LDA is considerably distorted (should be
  about twice as wide). Likewise fig. 6.16 and others.

- P. 22 eq. 2.3: x^t,r^t -> (x^t,r^t) (ie, a set of ordered pairs).
- P. 36 eq. 2.17: there should be some space between \bar{x} and \bar{r},
  otherwise it looks like \bar{x r} (ie, the average of the products xt*rt).
- P. 81 l. -8: E[g(x)] -> E_X[g(x)]
- P. 128 l. 1: the covariance is X'.X/N.
- P. 136: "their their".
- P. 175 l. 16: "that is inversely proportional to the distance" strictly
  means 1/d where d is the distance. It should say "that is a decreasing
  function of the distance".
- P. 176 l. 13: "The graph should always be connected". A disconnected graph
  still works, in that each connected component contains one (or more
  clusters). Generally, one should run a connected-components algorithm and
  then apply spectral clustering to each component.
- P. 177 first eq.: (xrj - zsj)^p -> |xrj - zsj|^p (absolute value).
- P. 177 l. -11: "constructing the minimal spanning tree of the graph" using
  Kruskal's algorithm.
- P. 199 l. 14: "seperating".
- P. 220 l. 14: the "calligraphic I" symbol (impurity?) has not been defined.
- P. 235, exe. 1: the Gini index should be multiplied by 2 to be consistent
  with eq. (9.5).
- P. 248 l. 1: should be log(y/(1-y)) (with extra parenthesis).
- P. 261 eq. (10.48): the [ ]+ operator should apply to w'.(xv-xu), not to
  xv-xu.
- P. 267: it is odd to consider MLPs as nonparametric methods.
- P. 310: "lingustics".
- P. 315: "Immenent".
- P. 326: "topogrophical".
- P. 350 point 5: we cannot solve analytically for the optimum; a QP requires
  an iterative algorithm. Heuristics for learning rates, etc. are less
  crucial than for other nonlinear models, but still important depending on
  the QP optimization algorithm.
- P. 352 line after eq. (13.3): "this is a standard quadratic programming
  problem". Also p. 353 l. 10: "quadratic programming methods".
- P. 355: "sectiona".
- Chapter 13: it would be more clear to show explicitly what variables are
  optimized over, eg in eq. (13.17) to write min_{w,w0,\rho,\xi_1...\xi_N},
  to differentiate it from variables such as \vu or N that are fixed and not
  optimized over.
- P. 370, l. -1: "we have r^t = w^T ..." (w instead of x).
- P. 497 l. -10: "a column of all 0s (or 1s)" -> of all -1s (or +1s). Also in
  "0101" and "1010".
- P. 557: "reproducable".