Climate simulations remain one of the best tools to understand and predict global and regional climate change. Yet, the accuracy of numerical climate models is constrained by computing power. Uncertainties in climate predictions originate partly from a poor or lacking representation of processes, such as ocean turbulence and clouds, that are not resolved in global climate models but impact the large-scale temperature, rainfall, sea level, etc. Representing these unresolved processes has been a bottleneck in improving climate simulations and projections. The explosion of climate data and the power of machine learning (ML) algorithms are suddenly offering new opportunities: can we deepen our understanding of these unresolved processes and simultaneously improve their representation in climate models to reduce climate projections uncertainty? This talk discusses the advantages and challenges of using machine learning for climate projections. The focus will be on recent work in which we leverage ML tools to learn representations of unresolved ocean processes – in particular, learning symbolic expressions. Some of this work suggests that machine learning could open the door to discovering new physics from data and enhance climate predictions. Yet, many questions remain unanswered, making the next decade exciting and challenging for ML + climate modeling for robust and actionable climate projections.