This is an introductory machine-learning course specifically developed with STEM students in mind. Our goal is to provide the interested reader with the basics to employ machine learning in their own projects and to familiarize themself with the terminology as a foundation for further reading of the relevant literature. In these lecture notes, we discuss supervised, unsupervised, and reinforcement learning. The notes start with an exposition of machine learning methods without neural networks, such as principle component analysis, t-SNE, clustering, as well as linear regression and linear classifiers. We continue with an introduction to both basic and advanced neural-network structures such as dense feed-forward and conventional neural networks, recurrent neural networks, restricted Boltzmann machines, (variational) autoencoders, generative adversarial networks. Questions of interpretability are discussed for latent-space representations and using the examples of dreaming and adversarial attacks. The final section is dedicated to reinforcement learning, where we introduce basic notions of value functions and policy learning.

翻译：我们的目标是向感兴趣的读者提供基本和先进的神经网络结构,例如密集的进料和常规神经网络、经常性的神经网络、限制的波尔兹曼机器、(变式)自动装饰机、基因对抗网络。关于可解释性问题的讨论,用于潜居空间的表述和使用梦想和对抗攻击的例子。最后一节专门论述强化学习,我们在这里介绍价值功能和政策学习的基本概念。