Amy Howe

Submitted by admin on
Primary Title:  
Writing Academic Director, Casual
Institution:  
Smith College
Department:  
Precollege Programs
Email Address:  
ahowe30@smith.edu

Kevin Geryk

Submitted by admin on
Primary Title:  
Custodian (Evenings)
Additional Title:  
Relief Custodian (Evenings)
Institution:  
Smith College
Department:  
Facilities Management
Additional Department:  
Facilities Management
Email Address:  
kgeryk@smith.edu

Kathleen Pertzborn

Submitted by admin on
Primary Title:  
Vice President and Chief of Staff
Institution:  
Amherst College
Department:  
Office of the President
Email Address:  
kpertzborn@amherst.edu
Office Building:  
Converse Hall
Office Room Number:  
Room 104A

Olivia Melendez-Lawren

Submitted by admin on
Primary Title:  
Global Education Assistant
Institution:  
Amherst College
Department:  
Global Education Office
Email Address:  
omelendezlawren@amherst.edu
Telephone:  
+1 (413) 542-5691
Office Building:  
Converse Hall
Office Room Number:  
Room 202

Honors Research

The Commonwealth Honors College thesis or project is intended to provide students with the opportunity to work closely with faculty members to define and carry out in-depth research or creative endeavors. It provides excellent preparation for students who intend to continue their education through graduate study or begin their professional careers. The student works closely with their 499Y Honors Research sponsor to pursue research on a topic or question of special interest to them in preparation for writing a 499T Honors Thesis or completing a 499P Honors Project.

S-Bayesian Deep Learning

This seminar will introduce students to research in the area of Bayesian methods applied to deep neural network models. The course will begin with foundational readings on Markov chain Monte Carlo and variational Bayesian methods and proceed to cover recent advances that are enabling the application of Bayesian inference to increasingly large deep learning models. The course will also cover methods for accelerating prediction using Bayesian deep learning models and for evaluating Bayesian deep learning models.
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