  Study of the production of pairs of top quarks in association with a Higgs
boson is one of the primary goals of the Large Hadron Collider over the next
decade, as measurements of this process may help us to understand whether the
uniquely large mass of the top quark plays a special role in electroweak
symmetry breaking. Higgs bosons decay predominantly to \bbbar, yielding
signatures for the signal that are similar to $t\bar{t}$ + jets with heavy
flavor. Though particularly challenging to study due to the similar kinematics
between signal and background events, such final states ($t\bar{t} b \bar{b}$)
are an important channel for studying the top quark Yukawa coupling. This paper
presents a systematic study of machine learning (ML) methods for detecting
$t\bar{t}h$ in the $h \rightarrow b\bar{b}$ decay channel. Among the eight ML
methods tested, we show that two models, extreme gradient boosted trees and
neural network models, outperform alternative methods. We further study the
effectiveness of ML algorithms by investigating the impact of feature set and
data size, as well as the structure of the models. While extended feature set
and larger training sets expectedly lead to improvement of performance, shallow
models deliver comparable or better performance than their deeper counterparts.
Our study suggests that ensembles of trees and neurons, not necessarily deep,
work effectively for the problem of $t\bar{t}h$ detection.
