CSE 6363: Machine Learning

Fall 2015

Location: ERB 129. Time: Mondays 7:00pm - 9:40pm

Instructor: Dr. Chris Ding, 529 ERB Hall. Email: chqding@uta.edu
Office Hours: Mon/Wed 3:30 - 5:00 (and by appointment).

Teaching Assistant: Di Ming Email: diming@mavs.uta.edu
Office Hours: 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, with total 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: classification, clustering, 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.

Prerequisites:

Undergrad level Linear Algebra
Undergrad level Statistics

Outline and Schedule

Textbook:

Additional Textbook/reference books:

Elements of Statistical Learning,
T. Hastie, R. Tibishirani , J. Friedman
2nd edition, Srpinger, 2009
(Available online)

Grades

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.

Academic Dishonesty

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

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2001-08-20