The UCSF Bakar Institute is partnering with the UC Berkeley D-Lab to offer an introductory deep learning workshop for UCSF faculty, students, and staff.

This workshop introduces the basic concepts of Deep Learning - the training and performance evaluation of large neural networks. Like many other machine learning algorithms, we will use deep learning algorithms to map input data to their appropriately classified outcome labels. You will use the R interface to Keras to become familiar with basic concepts like input and output layers, batch sizes and output dimensions, dropout rates, weight parametrization and bias, backpropagation, and loss, activation, and optimization functions for image classification applications in biomedicine. You will also gain confidence exploring more complex approaches that utilize pretrained and fine-tuned models.

By registering to attend, you are agreeing to provide feedback on the workshop to the organizers in follow-up communications.

About the Instructors:

Evan Muzzall earned his PhD in Biological Anthropology from Southern Illinois University Carbondale where he focused on spatial patterns of skeletal and dental variation in two large necropoles of Iron Age Central Italy (1st millennium BC). His current research focuses on how environmental and cultural influences affect "normal" skeletal and dental developmental trajectories and various machine learning topics. He is the Instructional Services Lead at the D-Lab, teaches several of the R workshop trainings, and helped found the Machine Learning Working Group in Fall 2016.

Chris Kennedy is a PhD student in biostatistics at UC Berkeley, and an independent data science consultant. He is also a D-Lab instructor and consultant, a Berkeley Institute for Data Science Fellow, a Biomedical Big Data Fellow, and a graduate researcher in the Integrative Cancer Research Group. His methodological interests encompass machine learning, randomized trials, targeted causal inference, deep learning, & text analysis. His applications are primarily in precision medicine, public health, genomics, and election campaigns.

Intro to Deep Learning