代做MATH 4720 W03 STATISTICAL DATA MINING帮做Python语言

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