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40Targeted learning: From MLE to TMLEMark van der LaanDivision of Biostatistics, School of Public HealthUniversity of California, Berkeley, CAIn this chapter I describe some of the essential elements of my past scientificjourney from the study of nonparametric maximum likelihood estimation(NPMLE) to the field targeted learning and the resulting new general tooltargeted minimum loss based estimation (TMLE). In addition, I discuss ourcurrent and future research program involving the further development of targetedlearning to deal with dependent data. This journey involved masteringdifficult statistical concepts and ideas, and combining them into an evolvingroadmap for targeted learning from data under realistic model assumptions.Ihopetoconveythemessagethatthisisahighlyinspiringevolvingunifyingand interdisciplinary project that needs input for many future generations tocome, and one that promises to deal with the current and future challengesof statistical inference with respect to a well-defined typically complex targetedestimand based on extremely highly dimensional data structures perunit, complex dependencies between the units, and very large sample sizes.40.1 IntroductionStatistical practice has been dominated by the application of statistical methodsrelying on parametric model assumptions such as linear, logistic, and Coxproportional hazards regression methodology. Most of these methods use maximumlikelihood estimation, but others rely on estimating equations such asgeneralized estimating equations (GEE). These maximum likelihood estimatorsare known to be asymptotically Normally distributed, and asymptoticallyefficient under weak regularity conditions, beyond the key condition that thetrue data generating distribution satisfies the restrictions assumed by theseparametric models.When I started my PhD in 1990, my advisor Richard Gill inspired meto work on maximum likelihood estimation and estimating equation meth-465