- Professor: Victoria Manfredi,
vumanfredi [at] wesleyan.edu,
- Meetings: Th/Fr afternoon
- In-person office hours (in my office):
Mon 3:30-5p, Tu 4:30-6p, Wed 4:30-6p, and by appointment.
- Virtual office hours (on Zoom):
Email me to set-up a time to meet, sometime within my in-person office hours if possible.
- Announcements and online discussion:
We'll use Google Classroom. I will add you, but you should double-check that you've been added and receive announcements, etc.
This tutorial provides an introduction to the field of machine learning:
the first half of the tutorial will focus on specific regression and
classification techniques while the second half of the tutorial will focus on
deep neural networks and reinforcement learning. The tutorial readings will be
taken from several machine learning textbooks, and will be complemented by a
set of slides. There will be weekly meetings to discuss the material and work
on programming projects.
This class has COMP 211, COMP 212, and MATH 228 as pre-requisites.
We will cover concepts from probability and linear algebra as needed to ensure the
course is self-contained. Programming will be done in python.
Your grade will be based on approximately 10 homework assignments (80%), your comments on my slides and homework assignments (10%), and writing up questions and answers for one homework in latex (10%).
Assignments will be posted on the
All work must be submitted electronically.
Grades and feedback on code submissions will be returned as paper, with
written work and code printed out with
comments written on it directly. I will email you your grades periodically
to double-check that the information that I have is consistent with what you have.
It is your responsibility to check your grades and feedback and report any
issues promptly. It is always possible for mistakes to happen in
recording scores, especially in larger classes. If I do not hear about a
problem, it will not be fixed.