Machine learning (ML) models have gained much attention for solving static problems such as computer vision thanks to their efficiency and generalization ability in extracting knowledge and patterns from stationary objects. However, the world is constantly changing: emerging challenges for artificial intelligence lie in the realm of dynamical systems, where it is crucial to absorb new knowledge and learn temporal evolutions. With the flexibility to capture the world's dynamics, ML models usually achieve statistically satisfactory performance during the learning process. However, the real-world applications are diverse and complex with vulnerabilities such as simulation divergence or violation of certain prior knowledge, requiring novel design of the ML techniques to investigate and impose robustness and trust in an end-to-end and efficient manner. From an alternative perspective, many machine learning problems can be viewed as dynamical systems, with examples ranging from neural network forward propagation to optimization dynamics and countless problems with sequential data. These generate increasing interest to study the intrinsic, evolving dynamics of these problems, with the potential to come up with novel methodologies for theory development and their applications. The mission of the MLmDS workshop is to bring together researchers from diverse backgrounds including but not limited to artificial intelligence and dynamical systems, gathering insights from these fields to facilitate collaboration and adaptation of theoretical and application knowledge amongst them.