digiLab Academy: “AI in the Wild: Foundations in Machine Learning for Future Flight"

**提高未来飞行工程师和分析师的技能**我们正在建立一个为期3个月的兼职课程，这将提高初级未来飞行工程师和分析师在工作场所的技能。它被称为“野外人工智能：未来飞行机器学习的基础”。这些工程师/分析师通常拥有关于他们的系统的特定领域知识，并且熟悉他们的数据。但国家AI/ML技能差距意味着超过90%的数据甚至没有被使用，有价值的见解被错过了。digiLab想要改变这一点。**课程结构**每个月将包括三周的讲座、练习和研讨会；在第四周，我们将提供面对面的“黑客马拉松”技能挑战，以便管理人员可以看到学习被付诸行动。我们也将积极寻找在未来飞行项目上进一步合作的机会。第一个月将教授机器学习的关键原理；第2个月，深度学习；第三个月，概率机器学习。**学习成果**它被称为“野外人工智能”有两个原因。首先，我们希望让学习者能够独立解决任何问题，“在野外”：这不仅包括如何使用机器学习工具，还包括如何处理新的、意想不到的、各种各样的数据挑战。其次，通过我们为未来飞行部门运营的组织提供的丰富咨询经验，我们知道，来自安全关键环境或物理复杂系统的许多数据都是“野生的”——也就是说，混乱或稀疏。这可能是由于传感器故障或无法获得足够的样本。因此，我们将教工程师如何降低计算成本，提高他们对模型输出的信心。**提高员工技能可以提高生产力、保留率和幸福感。我们的课程将加速未来飞行组织的业务成果，减少他们对昂贵的外部顾问的依赖，以填补内部技能差距。最终，英国将受益于人工智能/机器学习技能差距的缩小，以及通过智能使用数据加速未来飞行计划。

**Upskilling Future Flight Engineers and Analysts**We are building a 3-month, part-time course which will upskill junior Future Flight engineers and analysts in their workplace. It's called "AI in the Wild: Foundations in Machine Learning for Future Flight". These engineers/analysts typically possess domain-specific knowledge about their systems and familiarity with their data. But the national AI/ML skills gap means that over 90% of data isn't even being used, and valuable insights are being missed. digiLab wants to change this.**Course Structure**Each month will include three weeks of lectures, practice exercises, and seminars; in the fourth week, we will deliver an in-person "hackathon" skills challenge, so that managers can see learning being put into action. We will also be keen to identify opportunities for further collaboration on Future Flight projects. Month 1 will teach Key Principles in ML; Month 2, Deep Learning; and Month 3, Probabilistic ML.**Learning Outcomes**It's called "AI in the Wild" for two reasons. Firstly, we want to empower learners to be able to tackle any problem independently, "in the wild': this will involve teaching not just how to use ML tools, but also how to approach new, unexpected, and varied data challenges. Secondly, through our extensive experience consulting for organisations operating in Future Flight sectors, we know that much of the data coming out of safety-critical environments or physically complex systems is "wild" - that is, messy or sparse. This could be due to malfunctioning sensors or an inability to acquire sufficient samples. So we will be teaching engineers how to reduce computational cost and increase confidence in their model outputs.**Upskilling Benefits**Upskilling employees increases productivity, retention, and wellbeing. Our course will accelerate business outcomes in Future Flight organisations and reduce their dependency on expensive external consultants to plug the in-house skills gap. Ultimately, the UK will benefit from the closing of the AI/ML skills gap and the acceleration of the Future Flight programme through the intelligent use of data.