Syllabus: Signal Processing and Networking for Big Data Applications,

Zhu Han, ECE and CS Department, University of Houston

Instructor information

1.     Email: ,

2.     Syllabus

Course description and objective

In this course, we plan to address the challenges from the management of the big data, through the lens of signal processing. It should be noted that the term signal processing here is not limited to the processing of the traditional analog or digital signals, but rather should be understood as a wide range of computational and/or analytical techniques for transformation and interpretation of information. Therefore this course will focus on various theories and techniques that help make sense of the Big Data, as well as their applications on various engineering domains, such as machine learning, networking, energy systems, and so on. There are three main objectives of writing this course. The first objective is to provide an introduction to the big data paradigm, from the signal processing perspective. The second objective is to introduce the key techniques to enable signal processing for big data in a comprehensive way. The third objective is to present the state-of-the-art big data applications. This will include classifications of the different schemes and the technical details in each scheme.


Required: Signal Processing and Networking for Big Data Applications, Cambridge University Press, 2017

Supplementary: Zhu Han, Husheng Li, and Wotao Yin, Compressive Sensing for Wireless Networks, Cambridge University Press, UK, 2013.













Course content and calendar

  1. Overview (1.5 hours), slides,
  2. Basics Review (1.5 hours), slides,
  3. Big Scale Optimization: block structured (1.5 hours), slides,
  4. Big Scale Optimization: ADMM (1.5 hours), slides,
  5. Big Scale Optimization: sparsity (1.5 hours), slides,
  6. Big Scale Optimization: finite sum (1.5 hours), slides,
  7. Big Scale Optimization: Mixed Integer Programming (1.5 hours), slides,
  8. Big Scale Optimization: applications (3 hours), slides,
  9. Deep Learning Basics: (3 hours), slides,
  10. Deep Learning Regulation and Optimization (3 hours), slides,
  11. Deep Learning CNN and RNN details (3 hours), slides,
  12. Deep Learning Methodology and Applications (1.5 hours), slides,
  13. Deep Learning Advanced Topics, slides
  14. Sublinear Algorithm (1.5 hours), slides,
  15. Bayesian Nonparametric Learning (1.5 hours), slides,
  16. Tensor (1.5 hours), slides,
  17. Software: Tensorflow, MapReduce, Spark, Hadoop. (1.5 hours), slides,