\(\newcommand{\bbO}{\mathbb{O}}\) \(\newcommand{\bbD}{\mathbb{D}}\) \(\newcommand{\bbP}{\mathbb{P}}\) \(\newcommand{\bbR}{\mathbb{R}}\) \(\newcommand{\Algo}{\widehat{\mathcal{A}}}\) \(\newcommand{\Algora}{\widetilde{\mathcal{A}}}\) \(\newcommand{\calF}{\mathcal{F}}\) \(\newcommand{\calM}{\mathcal{M}}\) \(\newcommand{\calP}{\mathcal{P}}\) \(\newcommand{\calO}{\mathcal{O}}\) \(\newcommand{\calQ}{\mathcal{Q}}\) \(\newcommand{\defq}{\doteq}\) \(\newcommand{\Exp}{\textrm{E}}\) \(\newcommand{\IC}{\textrm{IC}}\) \(\newcommand{\Gbar}{\bar{G}}\) \(\newcommand{\one}{\textbf{1}}\) \(\newcommand{\psinos}{\psi_{n}^{\textrm{os}}}\) \(\renewcommand{\Pr}{\textrm{Pr}}\) \(\newcommand{\Phat}{P^{\circ}}\) \(\newcommand{\Psihat}{\widehat{\Psi}}\) \(\newcommand{\Qbar}{\bar{Q}}\) \(\newcommand{\tcg}[1]{\textcolor{olive}{#1}}\) \(\DeclareMathOperator{\Dirac}{Dirac}\) \(\DeclareMathOperator{\expit}{expit}\) \(\DeclareMathOperator{\logit}{logit}\) \(\DeclareMathOperator{\Rem}{Rem}\) \(\DeclareMathOperator{\Var}{Var}\)

Section 11 Closing words

The velocity of advances in machine learning make it an exciting time to work as a statistician. Clearly, statistical inference is more challenging when one considers the sort of infinite-dimensional statistical models that underlie these developments. Even defining some fundamental statistical notions, like efficiency, in these settings is a challenge. Constructing estimators that obtain these properties is more challenging still. We hope that this short guide has provided an approachable introduction to this exciting area of research.

Targeted learning is a vibrant and active field of research, with new developments happening along theoretical, applied, and computational axes. The tlverse software environment is actively being developed to provide researchers with new tools for utilizing these methods.