BMI 219 Deep Learning (2017)

Module: 2
Sponsoring Program: BMI
Administrator:Julia Molla

Course Number: BMI 219
Course Name: Deep Learning
Units: 3
Grading Option: S/U
Course Director: Mike Keiser

Additional Course Director(s): Nobuyuki Ota
Room Number: MH-2110
Campus: Mission Bay
Schedule: April 24-May 12, 2017; M, W, Th, F, 10:00 AM-12:00 PM
Prerequisites: None
Maximum Class Size: 10

Course Description: Through lectures, hands-on exercises, and programming homeworks, this course will introduce students to current applications of deep learning in biological research. The syllabus covers the fundamentals of feedforward neural networks as well as a variety of more sophisticated and/or recently-developed neural networks useful in biological research such as convolutional neural networks (CNN), long short-term memory (LSTM) networks, variational autoencoders (VAE), and quasi recurrent neural networks (QRNN). Programming projects and examples use Chainer (, a Python framework for neural networks. Substantial Python experience or the equivalent is a prerequisite. The goal of the course is to cover the theory and practice of designing, using, and evaluating deep learning models for varied biological problems