Convolutional Neural Networks

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Optical Machine Learning: Harnessing Multiple Scattering for Efficiency

Explores the use of optics in machine learning, focusing on large-scale random matrix multiplication through multiple scattering of light.

Image Processing I: Convolutional Neural Networks

Introduces convolutional neural networks for image processing, covering basic components, architectures, and practical applications, including denoising and segmentation.

Neural Signals and Signal Processing

Explores neural signals, EMG processing, muscle synergies, and prosthetic control using advanced signal processing techniques.

Neural Signals and Signal Processing

Explores neural signal processing for brain-computer interfaces, including decoding techniques like Kalman filters and spike sorting.

Neural Signals and Signal Processing

Delves into neural signals, GLM, ANOVA, brain mapping, connectivity, and multivariate methods.

Digital Filtering: Butterworth Filter Design

Explores digital Butterworth filter design, covering scaling, stable poles, and filter application.

Frequency Response Analysis: Linear Systems and Filters

Covers the frequency response of linear systems and filter design principles.

Neural Signals and Signal Processing

Explores nuclear magnetic resonance, MRI principles, pulse sequences, image reconstruction, safety considerations, and volume normalization in brain imaging.

Nonlinear Supervised Learning

Explores the inductive bias of different nonlinear supervised learning methods and the challenges of hyper-parameter tuning.

Neural Signals and Signal Processing: Methods and Designs

Explores neural signal processing methods, fMRI designs, GLM analysis, and quality control.