Instructor: Jayadev Acharya, 382 Rhodes Hall
Office hours: Mo 10-11, 380 Rhodes Hall
Sourbh Bhadane, OH: We 2-3, 380 Rhodes Hall
Yujia Ma, OH: Tu 4.30-5.30, 380 Rhodes Hall (starting 2/5)
Ziteng Sun, OH: Th 4-5.30, 380 Rhodes Hall
Lectures: MWF 11:15-12:05, 203 Phillips Hall
Discussion: Tu 9:05-9:55, 203 Phillips Hall
The course is devoted to the understanding the mathematical
underpinnings of machine learning, and consider some applications
within ECE. The first half of the course will provide basics of
supervised learning, unsupervised learning, and learning
theory. The second half will focus on some applications in some
fields with ECE.
A break-up of the course coverage is the following:
Supervised Learning: 5 weeks
Unsupervised Learning: 2 weeks
Learning Theory: 1.5 weeks
Engineering applications: 4 weeks
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.
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
- Machine Learning, Tom Mitchell
Almost weekly assignments.
We will use Piazza for announcements, discussions, and for posting materials. We will use CMS for uploading assignments.
We will have ten assignments.
- The lowest score will be
- You will have six late days that you can choose to use as
you see fit without any penalty. Late days are counted in quantums
of one day.
||Due date (eod)
The midterm will be held on 3/14 at 7.30 PM
Tentative Syllabus (subject to
Decision Trees, Naive Bayes, Linear classifiers,
Perceptron, Logistic regression, Linear regression, Regularization,
SVM, Kernel trick, Bagging, Boosting, Bias variance
trade-offs, Neural networks
Principal Components Analysis, Johnson Lindenstrauss Lemma, Clustering
VC dimension, generalization bounds
Hidden Markov Models, Viterbi Algorithm, Speech recognition,
Compressive Sensing and sparsity,
Collaborative Filtering, Managing data centers/cloud
We will use python as the programming language. Familiarity with
python will be helpful, but not necessary. Please install Python,
and play around. How to install:
We will use scipy and sci-kit-learn, which come with an anaconda
update, but need manual installation for the second option (use pip).
Please install Jupyter. It is an
interactive (and awesome) way to do ML using Python. It might be
easier to install with Anaconda, but if you can installed Python the
hard way, you should be able to install it too.