Bayesian Deep Learning

Tufts CS Special Topics Course | COMP 150 - 03 BDL | Fall 2018


Jump to: SoftwareConferences & WorkshopsRelated CoursesPrereq CatchupDeep Learning Self-study Resources


For this course, we strongly recommend using a custom environment of Python packages all installed and maintained via the free ['conda' package/environment manager from Anaconda, Inc.]

For detailed instructions, see the [Python Setup Instructions page]

High Performance Computing

For your final project, you have two options:

  • use the Tufts High performance computing cluster
  • use Amazon Web Services (AWS)

If you are looking for basic workshops to get help on things like using Linux or the Tufts High-performance computing cluster, checkout the programs in the Tufts Data Lab:

Conferences and Workshops

Bayesian Deep Learning workshop at NIPS

Good place to browse for potential project ideas:

Related Courses

Summer School on Deep Learning AND Bayesian Methods

August 27th – September 1st 2018, Moscow, Russia

Prereq Catchup Resources

Here are some useful resources to help you catch up if you are missing some of the pre-requisite knowledge. Please contribute new resources by starting a topic on the Canvas discussion forum.

To achieve this objective, we expect students to be familiar with:


First-order gradient-based optimization

Linear algebra

Basic supervised machine learning methods

  • Key concepts:
    • Linear regression
    • Logistic regression

Deep Learning Self-study Resources

Here are some related free online courses which would be good introductions to standard deep learning methods: