When a self-adaptive system detects that its adap-tation goals may be compromised, it needs to determine how to adapt to ensure its goals. To that end, the system can analyze the possible options for adaptation, i.e., the adaptation space, and pick the best option that achieves the goals. Such analysis can be resource and time consuming, in particular when rigorous analysis methods are applied. Hence, exhaustively analyzing all options may be infeasible for systems with large adaptation spaces. This problem is further complicated as the adaptation options typically include uncertainty parameters that can only be resolved at runtime. In this paper, we present a machine learning approach to tackle this problem. This approach enhances the traditional MAPE-K feedback loop with a learning module that selects subsets of adaptation options from a large adaptation space to support the analyzer with performing efficient analysis. We instantiate the approach for two concrete learning techniques, classification and regression, and evaluate the approaches for two instances of an Internet of Things application for smart environment monitoring with different sizes of adaptation spaces. The evaluation shows that both learning approaches reduce the adaptation space significantly without noticeable effect on realizing the adaptation goals.