Explores designing integration scenarios for flipped and blended learning with digital materials, emphasizing active learning and structured environments.
Explores the intersection between neuroscience and machine learning, discussing deep learning, reinforcement learning, memory systems, and the future of bridging machine and human-level intelligence.
Examines perceptual modeling and spatial thinking in visual intelligence, exploring theories, cognitive maps, and the interplay between bottom-up and top-down processing.
Explores deep learning for autonomous vehicles, covering perception, action, and social forecasting in the context of sensor technologies and ethical considerations.
