Covers image processing techniques including noise addition, filtering, and image enhancement using various filters and tools.
Explores electrical metrology, covering random variables, noise sources, and their impact on electronic devices.
Explores different types of noise in devices and circuits, including interference noise, inherent noise, and random signals.
Introduces the basics of supervised learning, focusing on logistic regression, linear classification, and likelihood maximization.
Covers scientific image processing fundamentals, software practices, and ethical considerations in image processing.
Explores different types of noise in electrical systems and their impact on electronic devices.
Covers CNNs, RNNs, SVMs, and supervised learning methods, emphasizing the importance of tuning regularization and making informed decisions in machine learning.
Explores electronic, thermomechanical, and amplifier noise, calibration of amplitude, frequency tracking, and system limits.
Introduces deep learning fundamentals, covering data representations, neural networks, and convolutional neural networks.
Covers the basics of image processing for microscopy, including acquiring, correcting defects, enhancing images, and extracting information.
