Download STAT5850: Applied Data Mining Fall 2016 Syllabus

STAT5850: Applied Data Mining
Fall 2016 Syllabus
3 credit hours
Schedule
Wednesday: 7:00pm – 9:30pm
Rood Hall 2275
Instructor
Dr. Hyunkeun (Ryan) Cho
[email protected]
Everett Tower 5504
Office Hours
Tuesday: 1:30pm – 3:30pm or by appointment
Course Webpage
http://www.stat.wmich.edu/~hzz3534/stat5850
Textbook
An Introduction to Statistical Learning with Applications in R, by James, Witten, Hastie and Tibshirani is required,
downloadable at WMU library for FREE (http://link.springer.com/book/10.1007%2F978-1-4614-7138-7).
Reference
The Elements of Statistical Learning, by Hastie, Tibshirani and Friedman is recommended, downloadable at WMU
library for FREE (http://link.springer.com/book/10.1007%2F978-0-387-84858-7).
Data Mining with Rattle and R, by Williams is recommended, downloadable at WMU library for FREE
(http://link.springer.com/book/10.1007%2F978-1-4419-9890-3).
Description
Data mining is the art and science of intelligent data analysis. The aim is to discover meaningful insights and
knowledge from data. Discoveries are often expressed as models, and we often describe data mining as the
process of building models. A model captures, in some formulation, the essence of the discovered knowledge. A
model can be used to assist in our understanding of the world. Models can also be used to make predictions. For
the data miner, the discovery of new knowledge and the building of models that nicely predict the future can be
quite rewarding.
For the development of models, this course aims to go far beyond the classical statistical methods, such as linear
regression. This course aims to provide an applied overview to such modern non-linear methods as Generalized
Additive Models, Decision Trees, Boosting, Bagging and Support Vector Machines as well as more classical linear
approaches such as Logistic Regression, Linear Discriminant Analysis, K-Means Clustering and Nearest Neighbors.
At the end of this course, you should have a basic understanding of how all of these methods work and be able to
apply them in real situations.
Statistical Programming R
A statistics package is essential for such an applied course. There are very few packages that can implement all of
the different approaches we will cover. Of those that can, most are extremely expensive. The one that we will use
in this course is R. R has several advantages. In addition to supporting all of the statistical learning methods we will
cover, it is also the package of choice for research statisticians. This means that it is at the cutting edge with
respect to new methods. R is also an extremely flexible program. For example, one can use R to write own
functions to format data or implement new procedures. Finally, R is free so you can easily use it at any company
that you may end up at! R can be downloaded from http://www.r-project.org/.
Also make sure to install ISLR package, which includes the datasets used in the course book:
http://cran.rproject.org/web/packages/ISLR/index.html.
Grading Policy
Your grade for the course will be based on workshops, exams and project with the following weights:
Group workshop/ homework
Exam1
Exam2
Final project
30%
25%
25%
20%
Group workshop/homework
In line with the applied nature of this class, a large portion of the assessment will be made through group
workshops in-class every week and homework assignments. You must submit workshops or assignments by a
deadline and late one is not accepted. Two lowest workshop scores will be dropped.
Exams
There will be two in-class exams. The policy on make-up exams is very strict; you are not allowed to take an exam
early. A MISSED EXAM IS A ZERO SCORE.
Final project
There will be no final exam. Instead, there will be a presentation on a group project, in addition to a report. More
detail will be announced later.
Elearning
Elearning will be used to update grades and contact you.
Disabilities
If you have any sort of disability such that you require accommodations to participate in class, take exams, etc, let
me know so that we can make the appropriate arrangements. I'm happy to work with you, but I need to know. For
more information, see www.wmich.edu/disabilityservices.
Incompletes
Incompletes will only be given according to University and Departmental policy. An incomplete is not a substitute
for a failing grade; they are given only after completing a major portion of the coursework with a passing grade,
and circumstances beyond your control prevent you from completing the coursework.
Academic Integrity
Students are responsible for making themselves aware of and understanding the academic policies and procedures
in the Undergraduate and Graduate Catalogs that pertain to Academic Honesty. These policies include cheating,
fabrication, falsification and forgery, multiple submission, plagiarism, complicity and computer misuse. [The
policies can be found at http://catalog.wmich.edu under Academic Policies, Student Rights and Responsibilities.] If
there is reason to believe you have been involved in academic dishonesty, you will be referred to the Office of
Student Conduct. Students will be given the opportunity to review the charge(s). If you believe you are not
responsible, you will have the opportunity for a hearing. Students should consult with your instructor if you are
uncertain about an issue of academic honesty prior to the submission of an assignment or test.
Students and instructors are responsible for making themselves aware of and abiding by the “Western Michigan
University Sexual and Gender-Based Harassment and Violence, Intimate Partner Violence, and Stalking Policy and
Procedures” related to prohibited sexual misconduct under Title IX, the Clery Act and the Violence Against Women
Act (VAWA) and Campus Safe. Under this policy, responsible employees (including instructors) are required to
report claims of sexual misconduct to the Title IX Coordinator or designee (located in the Office of Institutional
Equity). Responsible employees are not confidential resources. For a complete list of resources and more
information about the policy seewww.wmich.edu/sexualmisconduct.
In addition, students are encouraged to access the Code of Conduct, as well as resources and general academic
policies on such issues as diversity, religious observance, and student disabilities:
•
Office of Student Conduct www.wmich.edu/conduct
•
Division of Student Affairs www.wmich.edu/students/diversity
•
University Relations Office http://www.wmich.edu/policies/religious-observances-policy
•
Disability Services for Students www.wmich.edu/disabilityservices