Covers convolutional neural networks, filter operations, and their applications in signal processing and image analysis.
Covers the fundamentals of detection and estimation theory, focusing on mean-squared error and hypothesis testing.
Explores t-tests, confidence intervals, ANOVA, and hypothesis testing in statistics, emphasizing the importance of avoiding false discoveries and understanding the logic behind statistical tests.
Covers linear regression, regularization, inverse problems, X-ray tomography, image reconstruction, data inference, and detector intensity.
Explores neural signals, brain imaging techniques, and brain organization, emphasizing the importance of understanding brain imaging methods and measuring brain signals noninvasively.
