| Contact Information | Course Description | Course Goals and Objectives | Course Outline | Calendar |
| Course Materials | Grading | Attendance and Participation | Assignments | Honesty |
Contact Information
| Professor | Richard J. Povinelli, Ph.D. |
| richard.povinelli@marquette.edu | |
| Homepage | http://povinelli.eece.mu.edu |
| D2L | https://d2l.mu.edu/ |
| Phone | 414.288.7088 |
| Office Hours | Haggerty Hall 221 |
| Extra | Can be deleted |
Course Description and Prerequisites
Chaos and Nonlinear Signal Processing is a seminar course that investigates recent research in temporal data mining. We will study methods for finding hidden structures in time series. These hidden structures are found using techniques such as phase space reconstruction, clustering, neural networks, and genetic algorithms. Once discovered these structures are useful for solving system classification problems and for predicting events in time series. Example application areas include motor diagnostics, heart arrhythmia classification, speech recognition, and financial time series prediction. In addition to the topics mentioned above, this course discusses machine learning, time series analysis, adaptive signal processing, wavelets, and nonlinear dynamics.Course Goals
- By the end of this course, you should...
- Be able to explain and apply the constituent technologies that form the TSDM framework.
- Be able to conduct independent research.
Course Objectives
- By the end of this course, you should...
- Be able to explain and apply basic wavelet techniques for analyzing time series data.
- Be able to explain and apply basic autoregressive integrative moving average (ARIMA) techniques for analyzing time series data.
- Be able to provide a definition of chaos.
- Be able to describe the differences between chaotic and stochastic systems.
- Be able to define data mining and explain at least one of the techniques used for data mining.
- Be able to explain a simple genetic algorithm.
- Be able to apply a simple genetic algorithm to a search problem.
- Be able to explain the basic concepts of Markov models.
- Be able to explain and apply the key TSDM concepts of events, temporal patterns, event characterization function, temporal pattern cluster, time-delay embedding, phase space, augmented phase space, objective function, and optimization.
- Be able to explain and apply phase space reconstruction techniques.
- Be able to choose appropriate time delays for phase space reconstruction.
- Be able to choose appropriate embedding dimensions for phase space reconstruction.
- Be able to explain the basic concept of the follow chaos modelling and forecasting techniques: linear and nonlinear filters, Markov models, neural networks.
- Be able to explain in detail one of the following: chaos and nonlinear dynamics, evolutionary computation, Markov models, neural networks, or some other appropriate topic related to this course.
- Be able to conduct a literature review.
- Be able to critique others writing including journal articles, research proposals, journal article critiques, and conference papers.
- Be able critique others presentations.
- Be able to use online literature search tools.
- Be able to use a reference librarian as a resource for research.
- Be able to use the internet as a resource for research including newsgroups and appropriate webpages.
- Be able to write a conference level paper.
- Be able to find appropriate conferences and journals for publishing your work.
- Be able to conduct independent research in the TSDM area.
Course Outline
| What | When |
| Introduction to Time Series Data Mining (Povinelli-Chapter 1 and 9, Kantz-Chapter 1) | wk1 |
| Overview of TSDM concepts (Povinelli-Chapter 3, Kantz-Chapter 3) | wk2 |
| Markov Methods | wk3 |
| Introduction to using the library for research | wk3 |
| Fundamental TSDM Method (Povinelli-Chapter 4,5) | wk4 |
| Wavelets | wk5 |
| Genetic Algorithms | wk6 |
| Autoregressive Integrative Moving Average (ARIMA) methods (Povinelli-Chapter 2) | wk7 |
| Extended TSDM Methods (Povinelli-Chapter 6) | wk8 |
| Time delays for phase space reconstruction. (Kantz - Chapter 9) | wk9 |
| Embedding dimensions for phase space reconstruction.(Kantz - Chapter 6) | wk10 |
| Class choice for topics | wk11 |
| Class choice for topics | wk12 |
| Modelling Chaos (Kantz - Chapter 12) | wk13 |
| TSDM Applications (Povinelli-Chapter 7 and 8) | wk14 |
| Conference | wk15 |
Course Materials
Required Texts
- Nonlinear Time Series Analysis, 2nd Edition by Holger Kantz and Thomas Schreiber, Cambridge University Press, 2003.
Grading
| What | Number | Value per | Total |
| Article Reviews | 10 | 2.0% | 20% |
| Peer Review of Article Reviews | 10 | 1.0% | 10% |
|
2 | 1.5% | 3% |
|
2 | 1.0% | 2% |
| Conference Paper Draft | 2 | 3.5% | 7% |
| Peer Review of Paper | 2 | 2% | 4% |
| Conference Paper Final | 1 | 24% | 24% |
| Paper Presentation Final | 1 | 20% | 20% | Participation | 10% |
| Total | 100% |
To achieve an A in this class your conference paper must be of the caliber to be accepted by an appropriate conference. Every A caliber paper will be submitted to a conference with the instructor and the student's advisor as a coauthors.
NOTE: All dates and numbers are subject to change as deemed necessary!
Grade Scale
| 93+ | A |
| 90-93 | A- |
| 87-90 | B+ |
| 83-87 | B |
| 80-83 | B- |
| 77-80 | C+ |
| 73-77 | C |
| 70-73 | C- |
| 67-70 | D+ |
| 60-67 | D |
| 0-60 | F |
The grading scale is the most stringent one you will be held to, i.e. I can give you a higher letter grade than shown on the scale, but never a lower one. If you have missing assignments, you are inelligible to receive a higher grade.
Late Assignments
I will deduct 5% for assignments up to one day late, 10% for two days late, and 15% for up to three days late, and so on up to a maximum of 50% off. Assignments are due at the beginning of class. They are late after that. Assignments are not accepted after solutions have been distributed, nor after the last day of class. In class assignments are only accepted during the class period they are assigned.
Attendance and Participation
I have always enjoyed teaching classes where the students actively participate - a conversation is more fun than a monologue! Although there is no specific credit assigned for attending, it is still expected. There may be in class graded assignments. These may be turned in only during the class period they are given.Assignments
You should expect to spend, on average, from twelve (12) to fifteen (15) hours on article reviews, critiques, reading, homeworks, and other preparation for this class. This time is in addition to the three (3) hours of lecture you are expected to attend every week.
All written portions of assignments must be created using a word processor. No part of the writeup may be hand drawn. The assignments are to be well written with proper spelling and grammar. Points will be deducted for poorly written assignments. Written portions of assignments must be turned in as MS Word documents (.docx format). Code and other portions must be submitted in the proper electronic format. I will deducted 5% from incorrectly formatted assignments.
All assignments must be turned in via D2L. Assignments are due according to the the time specified in the D2L calendar.
Article Reviews
There will be ten (10) article reviews. This will help you understand the relevant literature for your research project. You will identify articles that are relevant to the course topic, if you are unsure of its relevance double check with the instructor. For each article you will write a 1 page summary and a 1 page critique.
Feedback
Critiquing others work is an excellent mechanism for improving your own. It is backbone of the peer review process. You will be expected to evaluating others work including critiquing others article reviews, research proposals, conference papers, and conference presentations.
Research project
There will be one (1) research projects. This will be an original research effort culminating in a conference quality paper and associated presentation. You will be expected to propose, execute, write up, and present independent and original research.
Exams
There will be no exams for this course.