Remove glossary

tex/2_lit/3_ml/3_cv.tex

@@ -5,7 +5,7 @@ Because ML models are trained by minimizing a loss function $L$, the
     resulting value of $L$ underestimates the true error we see when
     predicting into the actual future by design.
 To counter that, one popular and model-agnostic approach is cross-validation
-    (\gls{cv}), as summarized, for example, by \cite{hastie2013}.
+    (CV), as summarized, for example, by \cite{hastie2013}.
 CV is a resampling technique, which ranomdly splits the samples into a
     training and a test set.
 Trained on the former, an ML model makes forecasts on the latter.