Teaching

Winter 2025

BMI/BioE 212 Deep Learning for Biological and Clinical Research

This course will establish the foundations of deep learning through lectures, weekly seminars, and a hands-on approach in Python. We will cover the basics of regression and classification, model optimization, and neural network architectures, including autoencoders, convolutional networks, and transformers, with the use cases of these models in biological and clinical research.

Spring 2024

NS 219 The Encoder Decoder Framework for Neuroscience: Advanced Topics (2024)

Co-directors: Saul Kato, Karunesh Ganguli

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)

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)