Hybrid exoskeletons integrate powered exoskeletons and functional electrical stimulation (FES) to restore limb function and improve muscle capacity. However, technical challenges exist to customize the control of hybrid devices due to the nonlinear, uncertain gait and muscle dynamics of the human-machine system. Different from optimization techniques for gait control that leverage extensive model knowledge, this paper exploits a learning-based adaptive strategy to provide torque assistance about the hip and knee joints using a cable-driven exoskeleton with FES for treadmill walking. The human-machine system is modeled with phase-dependent switched pendular dynamics to capture gait phase transitions. A concurrent learning adaptive controller is designed to estimate a subset of the uncertain leg parameters during the swing phase to improve gait control. A sliding-mode controller provides robust leg support during stance. Stability of the overall switched system is proven using a multiple Lyapunov approach and dwell time analysis to guarantee exponential tracking and parameter estimation convergence across gait phase transitions.