Signal Processing (SP) and Machine Learning (ML) rely on good math and coding knowledge, in particular, linear algebra, probability, and complex numbers. A good grasp of these relies on scalar algebra learned in middle school. The ability to understand and use scalar algebra well, in turn, relies on a good foundation in basic arithmetic. Because of various systemic barriers, many students are not able to build a strong foundation in arithmetic in elementary school. This leads them to struggle with algebra and everything after that. Since math learning is cumulative, the gap between those without a strong early foundation and everyone else keeps increasing over the school years and becomes difficult to fill in college. In this article we discuss how SP faculty and graduate students can play an important role in starting, and participating in, university-run (or other) out-of-school math support programs to supplement students' learning. Two example programs run by the authors (CyMath at ISU and Ab7G at Purdue) are briefly described. The second goal of this article is to use our perspective as SP, and engineering, educators who have seen the long-term impact of elementary school math teaching policies, to provide some simple almost zero cost suggestions that elementary schools could adopt to improve math learning: (i) more math practice in school, (ii) send small amounts of homework (individual work is critical in math), and (iii) parent awareness (math resources, need for early math foundation, clear in-school test information and sharing of feedback from the tests). In summary, good early math support (in school and through out-of-school programs) can help make SP and ML more accessible.

翻译：信号处理与机器学习依赖于扎实的数学与编程知识，特别是线性代数、概率论和复数。掌握这些知识需要以中学阶段学习的标量代数为基础。而理解和熟练运用标量代数的能力，又取决于算术基础是否牢固。由于各种系统性障碍，许多学生在小学阶段未能建立坚实的算术基础，导致他们在代数及后续学习中举步维艰。鉴于数学学习具有累积性，早期基础薄弱的学生与其他学生之间的差距会随着学龄增长持续扩大，到大学阶段将难以弥补。本文探讨了信号处理领域的教师与研究生如何通过发起或参与大学（或其他机构）组织的校外数学辅助项目，在补充学生学习方面发挥重要作用。文中简要介绍了作者主导的两个示范项目（爱荷华州立大学的CyMath与普渡大学的Ab7G）。本文的第二个目标是从信号处理与工程教育者的视角出发——我们长期观察小学数学教学政策产生的深远影响——为小学提出几项近乎零成本的简易改进建议：（一）增加校内数学练习；（二）布置适量家庭作业（独立练习对数学学习至关重要）；（三）提升家长认知（包括数学资源获取、早期数学基础的重要性、清晰的校内测试信息及测试反馈的共享）。总而言之，优质的早期数学支持（通过校内与校外项目）有助于降低信号处理与机器学习的学习门槛。