Instructor
Dr. Lasanthi Watagoda
Associate Professor of Statistics
Class Meetings
Tuesday & Thursday
11:00 AM – 12:15 PM
Office Hours
Tuesday & Thursday
9:15 – 10:45 AM
Edwin Duncan Hall 123C
Prerequisite
STT 3851
This course serves as a continuation of STT 3851 (Statistical Learning I). Fundamentals of generalized linear models (GLMs) are explored, with emphasis on the linear exponential family, link functions, and applications to categorical and count response variables.
Supervised learning techniques for classification and tree-based methods are examined for both regression and classification problems. Unsupervised learning is introduced through principal component analysis and clustering methods.
Additional topics such as support vector machines, regression splines, and smoothing splines may be included. Students will complete projects using methods and algorithms covered in the course and communicate their results using professional tools and principles of reproducible statistical research.
By the end of the course, students should be able to:
Primary references
Computing
Required purchases
Students should obtain lawful access to the Frees textbook if it is not supplied through the University’s textbook rental or other course-material program. The ISLR text and the course computing software are available without a required individual software purchase.
Student performance will be evaluated using homework, quizzes, a project, a midterm examination, and a comprehensive final examination.
| Assessment | Weight |
|---|---|
| Homework | 15% |
| Quizzes | 30% |
| Project | 15% |
| Midterm Exam | 15% |
| Final Exam | 20% |
| Attendace | 05% |
| Total | 100% |
| Range | Grade | Range | 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 |
Students are expected to attend class regularly and participate actively in course activities. Attendance will be taken during class.
Students should communicate with the instructor as soon as possible when circumstances interfere with attendance and should follow Appalachian State University’s procedures for qualifying absences.
Students are expected to complete all assigned problems. Selected assignments, or selected parts of assignments, may be graded. Due dates are listed in the course pacing schedule.
Short quizzes will generally be given throughout the semester and will assess recent course material.
One midterm examination will assess material from the first major portion of the course.
The course project will require students to apply statistical-learning methods, use R appropriately, interpret results, and communicate findings in a professional and reproducible format.
A comprehensive final examination will cover material from throughout the course. The examination date and procedures will follow the University’s official final-examination schedule.
This course engages diverse scholarly perspectives in order to develop critical thinking, analysis, and debate. The inclusion of a reading, method, example, source, or other course material does not imply endorsement of the ideas or viewpoints it contains.
Students are expected to uphold Appalachian State University’s Academic Integrity Code and to contribute to an academic environment grounded in honesty, fairness, responsibility, and respect.
Appalachian State University is committed to accessible learning environments and equal opportunity. Students who need disability-related accommodations or academic adjustments should contact the Office of Access & Opportunity: Disability Resources.
Appalachian State University expects students to attend class and to remain responsible for work missed. University policies address religious observances, University-sponsored activities, emergency absences, and other qualifying circumstances.
University guidance indicates that students should generally expect substantial out-of-class preparation for each hour of class time. Success in this course requires consistent practice, reading, coding, and review outside scheduled class meetings.
Current University policy information:
Academic
Affairs — Policy and Statement Information
A separate Course Pacing document provides the detailed weekly schedule of topics, readings, homework, quizzes, examinations, project milestones, University breaks, and other important dates.
Fall 2026 instructional period: August 17 – December 2, 2026.
The detailed schedule is a planning guide and may be adjusted when necessary to support student learning or respond to University schedule changes. Material changes to course expectations will be communicated to students.
This syllabus is a guide to the course and does not constitute an express or implied contract. Course procedures or pacing may be adjusted when academically appropriate. Material changes will be communicated clearly to students.