This paper offers a phenomenological reading of contemporary machine learning through Heideggerian concepts, aimed at enriching practitioners' reflexive understanding of their own practice. We argue that this philosophical lens reveals three insights invisible to purely technical analysis. First, the algorithmic Entwurf (projection) is distinctive in being automated, opaque, and emergent--a metaphysics that operates without explicit articulation or debate, crystallizing implicitly through gradient descent rather than theoretical argument. Second, even sophisticated technical advances remain within the regime of Gestell (Enframing), improving calculation without questioning the primacy of calculation itself. Third, AI's lack of existential structure, specifically the absence of Care (Sorge), is genuinely explanatory: it illuminates why AI systems have no internal resources for questioning their own optimization imperatives, and why they optimize without the anxiety (Angst) that signals, in human agents, the friction between calculative absorption and authentic existence. We conclude by exploring the pedagogical value of this perspective, arguing that data science education should cultivate not only technical competence but ontological literacy--the capacity to recognize what worldviews our tools enact and when calculation itself may be the wrong mode of engagement.

翻译：本文通过海德格尔哲学概念对当代机器学习进行现象学解读，旨在丰富从业者对自身实践的反身性理解。我们认为这一哲学视角揭示了纯技术分析无法洞察的三个维度：首先，算法性的"筹划"具有自动化、不透明与涌现性的特征——这是一种无需明确表述或辩论的形而上学，通过梯度下降而非理论论证隐式固化。其次，即使最复杂的技术进步仍处于"集置"的支配之下，它们优化计算过程却从未质疑计算本身的优先性。第三，人工智能缺乏存在论结构（特别是"操心"的缺失）具有真正的解释力：这阐明了为何AI系统缺乏质疑自身优化命令的内在资源，以及为何它们能在没有"畏"的状态下进行优化——而这种焦虑在人类主体中正标志着计算性沉沦与本真存在之间的张力。最后，我们探讨了该视角的教学价值，主张数据科学教育不仅应培养技术能力，更应培育存在论素养——即识别工具所构建的世界观，以及判断何时计算本身可能成为错误的参与模式的能力。