Mathematics for Data Analysis & Decision Making

Class Time/Place: MWF 2:10 - 3:00PM Hoagland 168

Instructor: Jesus A. De Loera
TA: Lily Silverstein

TA: Roger Tian

Course Description: Data mining and Decision mathematical models are at the heart of successful applications such as information search (Google), airline-crew scheduling planning, social network analysis, bioinformatics. This course discusses the mathematics methods used in the analysis of data and for modeling to make optimal decisions. Methods include advanced linear algebra, optimization, probability, and geometry. These are some of the mathematical tools necessary for the data classification, machine learning, clustering and pattern recognition and for planning scheduling, and ranking. The course should be useful to those students interested in data sciences and in decisions models who wish to learn the basic mathematical theory used in algorithms and software.

References:

Optimization Models, by G. Calafiore and L. El Ghaoui, Cambridge, 2015

Matrix Methods in Data Mining and Pattern Recognition (Fundamentals of Algorithms), by Lars Elden, Published by SIAM
Note that this textbook has its official website: author's web site. There, you can find a lot of useful information (e.g., errata).

Here is the

Syllabus

Five Data Analysis and Decision Projects


Prerequisite and Expectations
Grading:
The grades will be calculated using the average and standard deviation of the class. 100 points are possible which will be divided as follows: Some important rules will be followed:

SOFTWARE and other RESOURCES:

An introduction to ZIMPL (the language used to program SCIP) is available in ZIMPL Manual. THe best way to learn it is to follow the numerous examples provided in the text.

For MATLAB, please take a look at the following highly useful MATLAB primers and tutorials.



HOMEWORKS & HANDOUTS

  • Homework 1, due April 17th 11:55pm:

    NOTE: Part 3 of the cancer problem, in posted version, was removed

  • Homework 2, due May 2nd 11:55pm:

    Click here for the data necessary to do the main project 2.

  • Homework 3, due May 16th 11:55pm:

  • Homework 4, due May 27 11:55pm:

  • final project, due June 4th 10:30 AM: