20 patterns, predictions, and actions
2
Bellhouse, “A New Look at Halley’s
Life Table,” Journal of the Royal Statistical
Society: Series A (Statistics in Society) 174,
no. 3 (2011): 823–32.
3
Ciecka, “Edmond Halley’s Life Table
and Its Uses,” J. Legal Econ. 15 (2008):
65.
4
Pearson and Pearson, “The History of
Statistics in the 17th and 18th Centuries
Against the Changing Background of
Intellectual, Scientific and Religious
Thought,” British Journal for the Philoso-
phy of Science 32, no. 2 (1981): 177–83.
to date are targeted advertising and digital content recommendation,
both of questionable value to society. Several scholars have explained
how the use of machine learning can perpetuate inequity through
the ways that it can put additional burden on already marginalized,
oppressed, and disadvantaged communities. Narratives of artificial
intelligence also shape policy in several high stakes debates about the
replacement of human judgment in favor of statistical models in the
criminal justice system, health care, education, and social services.
There are some notable topics we left out. Some might find that
the most glaring omission is the lack of material on unsupervised
learning. Indeed, there has been a significant amount of work on
unsupervised learning in recent years. Thankfully, some of the most
successful approaches to learning without labels could be described
as reductions to pattern recognition. For example, researchers have
found ingenious ways of procuring labels from unlabeled data points,
an approach called self supervision. We believe that the contents of
this book will prepare students interested in these topics well.
In writing this book, our goal was to balance mathematical rigor
against presenting insights we have found useful in the most direct
way possible. In contemporary learning theory important results
often have short sketches, yet making these arguments rigorous and
precise may require dozens of pages of technical calculations. Such
proofs are critical to the community’s scientific activities but often
make important insights hard to access for those not yet versed in
the appropriate techniques. On the other hand, many machine learn-
ing courses drop proofs altogether, thereby losing the important
foundational ideas that they contain. We aim to strike a balance, in-
cluding full details for as many arguments as possible, but frequently
referring readers to the relevant literature for full details.
Chapter notes
Halley’s life table has been studied and discussed extensively; for
an entry point, see recent articles by Bellhouse
2
and Ciecka,
3
or the
article by Pearson and Pearson.
4
Halley was not the first to create a life table. In fact, what Hal-
ley created is more accurately called a population table. Instead,
John Grount deserves credit for the first life table in 1662 based on
mortality records from London. Considered to be the founder of de-
mography and an early epidemiologist, Grount’s work was in many
ways more detailed than Halley’s fleeting engagement with Bres-
lau’s population. However, to Grount’s disadvantage the mortality
records released in London at the time did not include the age of the
deceased, thus complicating the work significantly.