CS464: Introduction to
Machine Learning Spring 2015
Course Information: ˇ
Instructor: Öznur Taştan ˇ
Office: EA429 ˇ
Office hours: by appointment Course Summary: Machine Learning is
centered on automated methods that improve their own performance through
learning patterns in data. Data is available and abundant in different
domains. In many of these data rich domains, machine learning provides cost
effective solutions and has had successful applications in business (speech
recognition systems, recommendation systems, information retrieval systems)
and applications in science (annotation of genome, predicting disease
biomarkers, etc.). In this undergraduate-level class, students will get an
introduction to the core mathematical and statistical techniques required to
understand some of the most common machine learning algorithms and some of
these algorithms. Topics include: supervised learning
(generative/discriminative learning, parametric/non-parametric learning);
unsupervised learning (clustering, dimensionality reduction), and some
additional topics (active learning, semi-supervised learning). The course
will draw examples from recent applications of machine learning. Programming
and theoretical assignments include hands-on practice with various learning
algorithms. The course project will give students an opportunity to work on a
problem of their interest. Students entering the class are expected to have a
pre-existing working knowledge of probability, statistics, programming and
algorithms. For other details please refer to the course syllabus [PDF]. Text
Book: No
required text book. Recommend
sources: ˇ
Kevin
P. Murphy, Machine Learning: a Probabilistic Perspective, The MIT
Press, 2012 (available in the bookstore). ˇ
Christopher
M. Bishop, Pattern Recognition and Machine Learning, Springer, 2011. ˇ
Tom
Mitchell, Machine Learning, McGraw Hill, 1997. ˇ
Ethem Apaydin,
Introduction to Machine Learning, 2e. The MIT Press, 2010. Course
website:
We
will be using Moodle. Please check regularly the Moodle page of the course
for lecture notes, homework assignments, project information, discussions and
announcements. |