Location: ERB 129. Time: Mondays 7:00pm - 9:40pm
Dr. Chris Ding, 529 ERB Hall. Email: email@example.com
Mon/Wed 3:30 - 5:00 (and by appointment).
Wed 3:30-5:00, ERB 204
About the Instructor
Dr. Ding is a world leading researcher in machine learning.
His work on k-means clustering, nonnegative matrix factorization,
L21 matrix norm, and feature selection are well-known and widely cited,
citation of 26934.
His paper "Feature Selection based
on mutual information ..." continues to be top-10 most popular papers in
IEEE Transactions of Pattern Analysis and Machine Intelligence (world no.1
journal in machine learning) since 2006. He has given invited seminars in
UC Berkeley, Stanford, CMU, U.Waterloo, U.Alberta, Google Research, IBM Research, Microsoft Research, etc.
See his bio.
- Contents and Objectives:
Machine Learning is a subfield/combination of computer science, statistics
and artificial intelligence.
It helps computers to learn knowledge/patterns from examples/data instances.
This course will cover the state-of-art machine learning techniques:
feature selection, dimension reduction, semi-supervised learning,
and neural network/deep learning.
This course is a study at advanced level.
We assumes the students had taken courses
such as "introduction to data mining", or
"Introduction to artificial intelligence", etc. We assumes students have
good skills in math, algebra, statistics, and basic graph theory/algorithm.
After completing this course, students will understand
machine learning at much deeper level, and
be able independently analyze data,
finding patterns in it, design and implement practical algorithms
to solve complex problems.
The course is designed with the goal of helping students to obtain
"data scientist" positions at major IT companies, and/or conduct
advanced research in machine learning.
Undergrad level Linear Algebra
Undergrad level Statistics
Outline and Schedule
Pattern Recognition and Machine Learning
- Additional Textbook/reference books:
The following textbooks are for reference purpose.
The mathematics level of the class is approximately same as
Elements of Statistical Learning,
T. Hastie, R. Tibishirani , J. Friedman
2nd edition, Srpinger, 2009
Course grades will be determined by the following weights:
6 quizzes (required) --- 60%
4 Class projects (required) --- 40%
Any student, found cheating on homeworks, projects, exams,
will get a lower grade below his/her usual grade,
from A to B, from B to C, from C to D, etc.
Class attendence is highly recommanded
Americans With Disabilities Act
The University of Texas at Arlington is on record as being committed to both the spirit and letter of federal equal opportunity legislation; reference Public Law 93112 -- The Rehabilitation Act of 1973 as amended. With the passage of new federal legislation entitled Americans With Disabilities Act - (ADA), pursuant to section 504 of The Rehabilitation Act, there is renewed focus on providing this population with the same opportunities enjoyed by all citizens.
As a faculty member, I am required by law to provide "reasonable accommodation" to students with disabilities, so as not to discriminate on the basis of that disability. Student responsibility primarily rests with informing faculty at the beginning of the semester and in providing authorized documentation through designated administrative channels.
It is the philosophy of The University of Texas at Arlington that academic dishonesty is a completely unacceptable mode of conduct and will not be tolerated in any form. All persons involved in academic dishonesty will be disciplined in accordance with University regulations and procedures. Discipline may include suspension or expulsion from the University.
"Scholastic dishonesty includes but is not limited to cheating, plagiarism, collusion, the submission for credit of any work or materials that are attributable in whole or in part to another person, taking an examination for another person, any act designed to give unfair advantage to a student or the attempt to commit such acts." (Regents’ Rules and Regulations, Part One, Chapter VI, Section 3, Subsection 3.2, Subdivision 3.22)
Student Support Services Available
The University of Texas at Arlington supports a variety of student success programs to help you connect with the University and achieve academic success. These programs include learning assistance, developmental education, advising and mentoring, admission and transition, and federally funded programs. Students requiring assistance academically, personally, or socially should contact the Office of Student Success Programs at 817-272-6107 for more information and appropriate referrals.