Subject: New Course: PHYS 476 Applied Machine Learning (Register now)

Description:
Please see new course below!

PHYS 476: Applied Machine Learning
Wednesdays, 4:00pm - 7:00pm
Room: PHYS 4221
Credits: 3

This one semester course introduces machine learning techniques that are
becoming pertinent in the technology industry. The course will focus on deep
learning using a hands-on approach and popular high-level libraries
(TensorFlow, Gensim, Keras, etc), and is designed for a broad audience of
intermediate students in related disciplines (any CMNS, Economics,
linguistics, etc.) in the sciences. It's goal is to give students an
understanding of the field and its capabilities, as well the tools to learn
the necessary extensions of the topic to apply it to their research.

Lectures will include introductions to Python and Linux, GPU acceleration,
cloud computing, neural nets, deep learning, natural language processing,
imagine recognition/computer vision, and AI safety.

Students are expected to have some background in functional programming,
linear algebra, calculus, and mathematical modeling. Some proficiency in
Python is strongly suggested. The course will be taught using a combined
lecture/laboratory approach, with coding exercises occurring periodically to
build basic proficiency with the techniques discussed in an informal group
environment.

For more information on the course, contact the instructors: Matt Severson
([log in to unmask]) and Justin Terry ([log in to unmask]). To register
for the course, send an email request to [log in to unmask]


Contact Person: Matt Severson ([log in to unmask]) Justin Terry
([log in to unmask]
Contact Email: [log in to unmask]