Covers the definitions and characteristics of transducers, sensors, and actuators in different physical domains, emphasizing sensor performance and measurement accuracy.
Covers the fundamentals of deep learning, including data representations, bag of words, data pre-processing, artificial neural networks, and convolutional neural networks.
Explores bug-finding, verification, and the use of learning-aided approaches in program reasoning, showcasing examples like the Heartbleed bug and differential Bayesian reasoning.