  This thesis demonstrate the efficacy of designing and developing machine
learning (ML) algorithms to selected use cases that encompass many of the
outstanding challenges in the field of experimental high energy physics.
Although simple implementations of neural networks and boosted decision trees
have been used in high energy physics for a long time, the field of ML has
quickly evolved by devising more complex, fast and stable implementations of
learning algorithms. The complexity and power of state-of-the-art deep learning
far exceeds those of the learning algorithms implemented in the CERN-developed
\texttt{ROOT} library. All aspects of experimental high energy physics have
been and will continue being revolutionized by the software- and hardware-based
technological advances spearheaded by both academic and industrial research in
other technical disciplines, and the emergent trend of increased
interdisciplinarity will soon reframe many scientific domains. This thesis
exemplifies this spirit of versatility and multidisciplinarity by bridging the
gap between ML and particle physics, and exploring original lines of work to
modernize the reconstruction, particle identification, simulation, and analysis
workflows. This contribution documents a collection of novel approaches to
augment traditional domain-specific methods with modern, automated techniques
based on industry-standard, open-source libraries. Specifically, it contributes
to setting the state-of-the-art for impact parameter-based flavor tagging and
di-Higgs searches in the $\gamma \gamma b\bar{b} $ channel with the ATLAS
detector at the LHC, it introduces and lays the foundations for the use of
generative adversarial networks for the simulation of particle showers in
calorimeters. These results substantiate the notion of ML powering particle
physics in the upcoming years and establish baselines for future applications.
