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Course Information
Course Title: STATISTICAL DATA MINING
Course Number and Section: MATH 4720 W03
Course Description
An introductory course to statistical data mining. It covers some fundamental concepts; popular techniques; and algorithms in statistical data mining. Topics include: supervised learning; unsupervised learning; probabilistic reasoning; regression; and nearest-neighbors; classification; model selection; component analysis; random forest; support vector machine; and clustering.
Prerequisite(s): MATH 3710 or Approved Petition Required
Course Level Student Learning Outcomes
Upon successful completion of this course, the student will be able to:
. Restate basic concepts and terminologies in statistical learning.
. Describe how and when learning works on practical problems.
. Implement some specific algorithms and methods in statistical learning.
. Apply some techniques in learning to real world data.
. Critically evaluate the results in the form of written reports and present them to classmates and others.
Instructional Technique(s)
This course mainly is taught by lectures. For the technical and implementing parts, I will ask students to use programming to run some simulation or real data analysis. During the lecuturing process, the students are welcome to give me any feedback or suggestions.
Required Textbooks and Materials
Signal Processing and Machine Learning with Applications
Michael M. Richter, Sheuli Paul, Veton Këpuska, Marius Silaghi Springer
2022
Bookstore Link: https://link.springer.com/book/10.1007/978-3-319-45372-9
Your Campus bookstore offers a Price Match guarantee. If you find our class texts or access codes cheaper at Booksmart, Barnes & Noble, or Amazon the campus bookstore will match the price at the time of purchase, or for up to 7 days after purchase. Search your course materials by the ISBN provided in this syllabus to assure that your price match is acceptable.
Topics and Assignments
|
Week/Unit |
Topics |
Assignments Due |
|
week 1 |
Digital Signal Representation |
to be announced in the lecture |
|
week 2 |
Signal Processing Background |
to be announced in the lecture |
|
week 3 |
Fundamentals of Signal Transformations |
to be announced in the lecture |
|
week 4 |
Digital Filters |
to be announced in the lecture |
|
week 5 |
Estimation and Detection |
to be announced in the lecture |
|
week 6 |
Adaptive Signal Processing |
to be announced in the lecture |
|
week 7 |
Spectral Analysis |
to be announced in the lecture |
|
week 8 |
midterm exam |
none |
|
week 9 |
General Learning |
to be announced in the lecture |
|
week 10 |
Signal Processes, Learning, and Recognition |
to be announced in the lecture |
|
week 11 |
Stochastic Processes |
to be announced in the lecture |
|
week 12 |
Feature Extraction |
to be announced in the lecture |
|
week 13 |
Unsupervised Learning |
to be announced in the lecture |
|
week 14 |
Markov Model and Hidden Stochastic Model |
to be announced in the lecture |
|
week 15 |
Audio Signals and Speech Recognition |
to be announced in the lecture |
|
week 16 |
final exam |
none |
Important Dates
For important dates, please consult the Academic Calendar via the following link: https://www.wku.edu.cn/en/academics/academic-calendar
Technical Requirements (if any)
1. In order for your Canvas course to function correctly, you need to use an appropriate internet browser, either Google Chrome or Firefox. It is best to use the most updated versions of these browsers.
2. Many students are eligible for a free MS Office Software Student Edition. To start the
application process, go to the Office 365 Education website. Eligible students are required to create an account and provide a valid Kean University ID to obtain access to the software applications.
3. Remember to download the latest versions of software used in this class.
Assessment
I. (for those skipping my lectures less than or equal to 2 times)
10%: homework, class participation, attendance, presentations;
40%: midterm exam 50%: final exam
II. (for those skipping my lectures 3 to 4 times)
10%: homework, class participation, attendance, presentations;
30%: midterm exam 35%: final exam
25%: oral exam
III. (for those skipping my lectures more than 5 times)
10%: homework, class participation, attendance, presentations;
20%: midterm exam 30%: final exam
40%: oral exam