Today's students are surrounded by machine learning (ML)-powered tools. Yet, few understand how they work. While there are numerous opportunities for students to learn about ML in informal settings (e.g., Alvarez et al., 2022; Druga et al., 2022) and online (e.g., code.org, Scratch), there are far fewer opportunities in the United States for students to learn about how ML works while they are in school. Although curriculum and standards frameworks have been developed to help educators navigate machine learning education, it is not yet known if teachers can or want to enact ML curriculum in their practice. As a former classroom teacher and ML graduate student, I saw interdisciplinary commonalities between machine learning and statistics, the subject I used to teach. In response, I co-designed a one-unit curriculum, "The Math of Facial Recognition" ("FaceID"), with a practicing high school teacher. We built the curriculum with three goals: (1) that it would aim to help teachers and students build high-level intuitions about ML, (2) that it would draw from teachers' and students' prior statistics knowledge, and (3) that it would bridge statistics and ML concepts by exploring facial recognition in smartphones, a ML application that students likely use everyday. We built custom web applications, termed "widgets," with the purpose of enabling students to explore and test hypotheses about facial recognition using images of themselves. Finally, we designed the curriculum with features that intentionally encouraged statistics teachers to adapt segments and activities of lessons for their students. That teachers would adapt a curriculum for instruction is not surprising; in fact, it is an expected component of good teaching. Drawing from studies in curriculum theory, I argue that teachers' adaptations of curriculum - from written documents, to teacher-intended curricula, to jointly-enacted curricula between teachers and students - are supported by their pedagogical design capacity (PDC) (Brown, 2009). In the context of my study, PDC refers to statistics teachers' diverse resources as designers of instruction: their statistics pedagogical content knowledge (PCK), subject-matter knowledge of statistics, students PCK, general knowledge of pedagogy, and their goals. Teachers' goals may or may not align with goals stated in the written curriculum, which can influence their adaptations. Two key goals of this work are to investigate how teachers' PDC supported their ability to adapt "FaceID," and to identify their rationales for making adaptations. To implement my study, I partnered with three experienced high school statistics teachers who agreed to enact "FaceID" in their classrooms. I observed each teacher's enactment, noting where and how teachers made adaptations. I interviewed each teacher before and after they taught the curriculum to learn about their prior knowledge, experiences, goals, and post-implementation, the nature of their adaptations. I also asked each teacher to identify their adaptations separately in a reflection document, because I was curious to understand how teachers defined an "adaptation" in their own practice. After concluding my field work, I analyzed the data to determine what adaptations teachers made to "FaceID" and their rationales for adapting segments and activities, taking note of instances where teachers interpreted their adaptations differently than the researchers. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://bibliotheek.ehb.be:2222/en-US/products/dissertations/individuals.shtml.]