News for
01-18-2019

Welcome to the Spring 2019 offering of Statistical Signal Processing. I have just posted the syllabus. I plan on some updates to the Chapter 1 intro lecture. I will be getting remaining chapters ready with new passwords. I am also considering adding an overview of machine learning to the course using the $5 book: Python Machine Learning. The first author of this book was my instructor at Scipy2017.

As the semester passes by continue to think about data science and machine learning, as these are very contemporary topics across many disciplines.

Fall 2019

A request has been made to offer the phase-locked loops course, ECE 5675 or perhaps wireless networking. This course, like 5615, can be a self-study course/independent study type course using the available lecture videos and course notes. Ask me for info. Other such courses are under consideration as well.

Office Hours

M 2:15 to 3:00 PM and after 4:20 PM as needed,
W 2:15 to 3:00 PM and after 4:20 PM as needed,
or by appointment.
Office EN 292,
Phone 255-3500, mwickert@uccs.edu.

Learning Python

Python Basics a tutorial written in Jupyter Notebook. ZIP.

Link to Anaconda. This is the scientific Python I recommend.

An IDE I recommend is Pycharm Community Edition.

NumPy2MATLAB and IPython reference card

Jupyter Lab is ready. Also see, Getting started with JupyterLab (Scipy2018).

Obtaining Mathematica

EAS RATS and LATS Servers

Mathematica is available across the campus due to the CU system wide site license. This system-site license also means that students may install their own copy on home computers as well. Some links of interest regarding the CU site license for Mathematica are: download and installation and support information.

Catalog Course Description

Concepts of signal processing using random signals; random vectors, random processes, signal modeling, Levinson recursion, Wiener filtering, spectrum estimation, and detection theory.
Prerequisite: ECE 4650/5650 or equivalent and ECE 3610 or equivalent.
Offered: Alternate Spring Semesters

Course Materials - Course Notes, m-Code

Course Syllabus as of 07:44 PM on Friday, January 18, 2019.

PDF file of Intro Lecture as of 06:55 AM on Wednesday, January 23, 2019.

Lecture Notes

  • PDF file of Chapter 2 as of 11:26 PM on Tuesday, January 22, 2019. Jupyter notebook as of 10:13 AM on Friday, March 24, 2017. Jupyter notebook pdf as of 03:24 PM on Wednesday, January 28, 2015.
  • PDF file of Chapter 3 as of 06:44 AM on Wednesday, January 18, 2017. Jupyter notebook as of 10:14 AM on Friday, March 24, 2017. Jupyter notebook pdf as of 08:56 PM on Wednesday, April 08, 2015.
  • PDF file of Chapter 4 as of 06:45 AM on Wednesday, January 18, 2017.
  • PDF file of Chapter 5 as of 02:47 PM on Tuesday, January 17, 2017. Levinson-Durbin IPYNB
  • PDF file of Chapter 6 as of 02:28 PM on Tuesday, January 17, 2017.
  • PDF file of Chapter 7 as of 02:48 PM on Tuesday, January 17, 2017.
  • PDF file of Chapter 8 as of 02:46 PM on Tuesday, January 17, 2017.
  • PDF file of Chapter 9 as of 02:29 PM on Tuesday, January 17, 2017.
  • PDF file of Chapter 10 as of 02:49 PM on Tuesday, January 17, 2017.
Lecture Videos - Download

Spring 2017 Lectures as MP4 Movies

All lectures, when available, are double length, meaning the class met for two lecture periods once per week. The .mp4 file size is typically 350 MB per 150 min lecture.

Problem Sets with Solutions
  • Set 1 as of 10:58 PM on Tuesday, January 22, 2019. Hints as of 06:42 AM on Wednesday, January 23, 2019
Python/Jupyter Projects
  • TBD
Takehome Exams