ECE 4200 Fundamentals of Machine Learning
Instructor: Jayadev Acharya, 382 Rhodes Hall
Office hours (Rhodes 310):
Mo 1.15-1.45 Jayadev Acharya
Tu 4.30-6.00 Huanyu Zhang
We 4.30-6.00 Ziteng Sun
Th 1.30-3.00 Yuhan Liu
Fr 4.00-5.30 Sourbh Bhadane
Lectures: Mo, We 8.40-9.55, Phillips Hall 101
Discussion: Fr 9.05-9.55, Phillips Hall 101
Overview
The course is devoted to the understanding how machine learning
works. We will gain hands on experience (yes, there is coding) as well as an
understanding of the theoretical underpinnings of the methods.
Prerequisites: Linear Algebra (Math2940 or equivalent), Basic
Probability and Statistics (ECE3100, STSCI3080, ECE3250 or
equivalent). The discussion sessions will be devoted to reinforce
various concepts that we will not cover but use in the lectures.
Grading
Assignments: 30%
Weekly assignments, assigned Wednesdays due Sunday
Miniproject: 22.5% (kaggle competition)
Midterm: 20%
Final: 27.5%
Late submission policy: TBD
We will use Piazza
for announcements, and discussions. We will use CMS for uploading
materials, and assignments.
Materials
We will take materials from various sources. Some books are:
-
Pattern Recognition and Machine Learning, Christopher Bishop
-
A Course in Machine Learning, Hal Daume III (available here)
-
Machine Learning: a Probabilistic Perspective, Kevin Murphy (available
online at Cornell Library)
-
The Elements of
Statistical Learning: Trevor Hastie, Robert Tibshirani, Jerome Friedman (available
here)
- Machine Learning, Tom Mitchell
Coding
We will use python as the programming language. Prior experience with
python can be helpful, but not necessary. Please install Python (Python.org),
and play around.
We will use scipy and sci-kit-learn packaces, and Jupyter Notebook. We will post on piazza instructions about them.