Spring 2024
NS 219 The Encoder Decoder Framework for Neuroscience: Advanced Topics (2024)
Many machine learning approaches can be thought of as a process of encoding high-dimensional data items into a low-dimensional space, then (optionally) decoding them back into a high-dimensional data space. This paradigm encompasses the endeavors of dimensionality reduction, feature learning, classification, and of particular recent excitement, generative models. It has even been proposed as a model of human cognition. This course will survey uses of encoder-decoder models in current neuroscience research. Lectures will be given by UCSF and other neuroscientists or machine learning practitioners.
Spring 2024
BMI 219 Deep Learning for Biological Research: Advanced Topics (2024)
Course Description: This three-week mini-course will establish the foundations of practical deep learning through a hands-on approach in Python. We will cover the basics of regression and classification, the optimization and training of neural networks, and model architectures including autoencoders, convolutional neural networks, and recurrent neural networks. The primary goal of this course is to equip students with the necessary foundations to apply basic neural networks to their own research. Experience with numerical computation in Python is required (or permission from the Instructor).
Spring 2023
BMI 219 Deep Learning for Biological Research: Advanced Topics (2023)
Spring 2022
BMI 219 Deep Learning for Biological Research: Advanced Topics (2022)
Spring 2021
BMI 219 Deep Learning for Biological Research: Advanced Topics (2021)