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.

Undergrad level Linear Algebra
Undergrad level Statistics

Outline and Schedule

Pattern Recognition and Machine Learning
Christopher Bishop
Springer, 2007.

Additional Textbook/reference books:
The following textbooks are for reference purpose.
The mathematics level of the class is approximately same as these textbooks.

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

Course grades will be determined by the following weights:

Class attendence is highly recommanded

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