MATH 710-01 [2589], Fall 2015
Mathematics of Big Data

Course information

Course:	MATH 710-01 [2589]: Mathematics of Big Data
Time/Place:	TuTh 4:00pm-5:15pm, MP 401
Instructor:	Dr.  Jacob Kogan
Office:	MP 426
Phone:	410-455-3297
Email:	kogan at math.umbc.edu
Office hours:	Tu, Th 2:15 PM-3:00 PM and by appointment

Course Description

Over the past decade, faced with modern data settings, off-the-shelf statistical machine learning methods are frequently proving insufficient. These modern settings pose three key challenges, which largely come under the rubric of big data:

• the data might have a large number of features,
• the data might have a large number of data instances,
• the data-types could be complex: such as permutations, or strings, or graphs

Large-scale machine learning problems demand scalable algorithms to extract patterns. We investigate novel mathematical algorithms to solve large-scale optimization problems that arise in machine learning and graph analysis. Machine learning in the context of extremely large datasets requires distribution of data and/or computation. We use proximal splitting methods to split a general machine learning problem into separate intermediate steps, each of which can be optimized or solved in closed form. The result is a class of first-order methods capable of converging to the general optimal solution. These methods are scalable to very large sizes, but the convergence rate can be extremely variable in ways that can be hard to predict.

• A series of introductory lectures covering basic Big Data/Data Mining techniques will be given.
• The lectures will be followed by a number of small projects (2-3 weeks per project) involving programming (students are welcome to use their favorite programming language). Students are encouraged to work on the projects in teams (no more than three students per team). You can upload your project.
• Students will be assigned papers to read and make a class presentation (students are welcome to pick their favorite papers for the presentation after the paper is approved by the instructor). Each student has to notify the instructor about the choice of paper by Tuesday, September 29, 2015. A delay with paper selection will carry a penalty of 2% off the presentation grade per each day of delay.

Course Objectives

To introduce students to the basic modern techniques of Big Data hadling.

1. solution

The Official UMBC Honors Code

By enrolling in this course, each student assumes the responsibilities of an active participant in UMBC's scholarly community in which everyone's academic work and behavior are held to the highest standards of honesty. Cheating, fabrication, plagiarism, and helping others to commit these acts are all forms of academic dishonesty, and they are wrong. Academic misconduct could result in disciplinary action that may include, but is not limited to, suspension or dismissal.

To read the full Student Academic Conduct Policy, consult the UMBC Student Handbook, the Faculty Handbook, the UMBC Integrity webpage www.umbc.edu/integrity, or the Graduate School website http://www.umbc.edu/gradschool/procedures/integrity.html.