An Introduction to Optimization with Applications in Machine Learning and Data Analytics

The  primary goal of this text is a practical one. Equipping students with  enough knowledge and creating an independent research platform, the  author strives to prepare students for professional careers. Providing  students with a marketable skill set requires topics from many areas of  optimization. The initial goal of this text is to develop a marketable  skill set for mathematics majors as well as for students of engineering,  computer science, economics, statistics, and business. Optimization  reaches into many different fields.

This text  provides a balance where one is needed. Mathematics optimization books  are often too heavy on theory without enough applications; texts aimed  at business students are often strong on applications, but weak on math.  The book represents an attempt at overcoming this imbalance for all  students taking such a course.

The book contains many  practical applications but also explains the mathematics behind the  techniques, including stating definitions and proving theorems.  Optimization techniques are at the heart of the first spam filters, are  used in self-driving cars, play a great role in machine learning, and  can be used in such places as determining a batting order in a Major  League Baseball game. Additionally, optimization has seemingly limitless  other applications in business and industry. In short, knowledge of  this subject offers an individual both a very marketable skill set for a  wealth of jobs as well as useful tools for research in many academic  disciplines.

Many of the problems rely on using a  computer. Microsoft’s Excel is most often used, as this is common in  business, but Python and other languages are considered. The  consideration of other programming languages permits experienced  mathematics and engineering students to use MATLAB® or Mathematica, and  the computer science students to write their own programs in Java or  Python.