Changho Suh – Textbook

Convex Optimization for Machine Learning Applications

  • A preliminary version (to be published)

  • Developed during the past three years while teaching EE523 Convex Optimization and EE424 Introduction to Optimization.

  • The contents are tailored for recent and trending topics in machine learning such as GANs and fair machine learning.

  • This book includes programming implementation of a variety of machine learning algorithms inspired by optmization fundamentals.

  • The implementation is based on Python, ScikitLearn and TensorFlow, and a brief tutorial of the used programming tools is also provided.

  • This book serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course.

  • Supported by the 2019 Google Education Grant

Information Theory for Data Science Applications

  • A preliminary version (to be published)

  • Developed during the past nine years while teaching EE623 Information Theory (seven times) and EE326 Introduction to Information Theory and Coding (twice).

  • The contents are tailored for recent and trending topics in data science such as social networks, ranking, GANs and fair machine learning.

  • This book includes programming implementation of a variety of algorithms inspired by fundamentals.

  • The implementation is based on Python and TensorFlow, and a brief tutorial of the used programming tools is also provided.

  • This book serves as a textbook mainly for a senior-level undergraduate course, yet is also suitable for a first-year graduate course.

  • Supported by the 2019 Google Education Grant

Digital Communication Principles for Data Science Applications

  • A preliminary version (to be published)

  • Developed during the past eight years while teaching EE321 Communication Engineering four times

  • This book makes an explicit connection between the communiation and data science problems, as well as to succinctly deliver the “story” of how the communication principles play a role for trending data science applications such as community detection, DNA sequencing, machine learning and speech recognition.

  • It includes programming implementation of a variety of algorithms inspired by fundamentals.

  • The implementation is based on Python and TensorFlow, and a brief tutorial of the used programming tools is also provided.

  • This book serves as a textbook for a junior and senior-level undergraduate course.

Probability for Information Technology Applications

  • A preliminary version (to be published)

  • Developed during 2021 Spring semeter while teaching EE210 Probability and Introductory Random Processes

  • This book succinctly relates the ‘‘story’’ of how the key principles of probability play a role, via classical and trending IT applications such as communication, social networks, machine learning and speech recognition.

  • The probabilistic concepts are delivered through many running examples, killer applications and Python coding exercises.

  • This book serves as a textbook for a sophomore-level undergraduate course, yet is also suitable for a junior or senior-level undergraduate course.