Instructor: Dr. Lasanthi Watagoda

Class meeting: TR 12:00pm - 01:45pm, WA209B

Office: WA340

In-person Office Hours: Tuesday/ Thursday 01:45 - 02:45 PM in WA340

Online Office Hours: Wednesday 10:30 - 11:30 AM. Click here to go to office hours

Prerequisite: STT 3851

Course Catelog Description:

This course serves as a continuation of STT 3851 (Statistical Learning I). Fundamentals of Generalized Linear Models (GLM) are explored, with a focus on the Linear exponential family, the Link function, and their applications to categorical and count response variables. Supervised learning techniques for classification models and tree-based methods are also examined, as they apply to both regression and classification problems. Unsupervised learning is introduced, including principal component analysis and various clustering algorithms. Other topics such as support vector machines, regression splines, and smoothing splines may be included. Students will complete projects using methods and algorithms covered in class and report their results using professional editing tools that use principles of reproducible statistical research.

Learning Outcomes

At the completion of this course, students will be able to:

Course Text:

Required Resources:

Text book, Computer with access to the RStudio server (provided), Note book, notes, pencil and a calculator

Homework: Students are expected to work all assigned problems. Only selected assignments or parts of assignments may at times be graded. See schedule for the dates.

Quizzes: I anticipate giving weekly quizzes during the semester. See schedule for the dates.

Tests/ Projects: One mid exam and a project will be given. See schedule for the dates.

Final Exam: A final exam will be given on TBA. This final exam will be comprehensive, covering all material that is covered in the course. As the final exam needs to be proctored you are required to have your video camera on during the final exam time.

Attendance and participation:

Attendance will be taken at the end of each class. Students are expected to attend every class. A detailed description of the ASU attendance policy can be found at the following link, [https://academicaffairs.appstate.edu/resources/syllabi-policy-and-statement-information]

Course Grading:

Range Letter Grade Range Letter Grade
92.50% and above A 72.50-77.49% C
90.00–92.49% A– 70.00-72.49% C–
87.50–89.99% B+ 67.50-69.99% D+
82.50–87.49% B 62.50-67.49% D
80.00-82.49% B– 60.00-62.49% D–
77.50-79.99% C+ 59.99% and below F

Academic Integrity Policy, Disability Services, and Religious Holiday Observance:

All students must abide by the ASU Academic Integrity Code posted at www.studentconduct.appstate.edu/ Those seeking accommodations for a disability should contact the Office of Disability Services (ODS) at http://www.ods.appstate.edu/ or 828-262-3056. The statements regarding University policies for students are posted at http://academicaffairs.appstate.edu/syllabi

University Policies

This course conforms with all Appalachian State University policies with respect to academic integrity, disability services, and class attendance. The details of the policies may be found at http://academicaffairs.appstate.edu/resources/syllabi.

Computers and Software

This course will use the RStudio server (https://mathr.appstate.edu/) that has the programs listed below and more installed.

You must have an active internet connection and be registered in the course to access the server. To access the server, point any web browser to https://mathr.appstate.edu/. You will need to acknowledge the connection is unsecure and possibly add a security exception to your web browser. Use your Appstate Username and Password to access the server. A screen shot of the RStudio server is shown below.