Artificial intelligence (AI) is often used to describe the automation of complex tasks that we would attribute intelligence to. Machine learning (ML) is commonly understood as a set of methods used to develop an AI. Both have seen a recent boom in usage, both in scientific and commercial fields.

For the scientific community, ML can solve bottle necks created by complex, multi-dimensional data generated, for example, by functional brain imaging or *omics approaches. ML can here identify patterns that could not have been found using traditional statistic approaches. However, ML comes with serious limitations that need to be kept in mind: their tendency to optimise solutions for the input data means it is of crucial importance to externally validate any findings before considering them more than a hypothesis. Their black-box nature implies that their decisions usually cannot be understood, which renders their use in medical decision making problematic and can lead to ethical issues.

Here, we present an introduction for the curious to the field of ML/AI. We explain the principles as commonly used methods as well as recent methodological advancements before we discuss risks and what we see as future directions of the field. Finally, we show practical examples of neuroscience to illustrate the use and limitations of ML.

人工智能 (AI) 通常用于描述复杂任务的自动化，我们将其归因于智能。机器学习 (ML) 通常被理解为一组用于开发人工智能的方法。最近，两者在科学和商业领域的使用都出现了蓬勃发展。

对于科学界来说，机器学习可以解决由功能性脑成像或组学方法等复杂的多维数据产生的瓶颈。机器学习可以识别使用传统统计方法无法发现的模式。然而，机器学习有一些需要牢记的严重局限性：它们倾向于优化输入数据的解决方案，这意味着在考虑任何发现而不是假设之前，从外部验证任何发现至关重要。它们的黑匣子性质意味着它们的决定通常无法被理解，这使得它们在医疗决策中的使用存在问题，并可能导致道德问题。

在这里，我们为对 ML/AI 领域感兴趣的人进行介绍。在讨论风险和我们认为该领域的未来方向之前，我们将解释常用方法的原理以及最近的方法进展。最后，我们展示了神经科学的实际例子来说明机器学习的用途和局限性。