R/Python/Matlab. (Basically, you need mathematical and computational maturity, and this can’t be your first course in stats!)A technical analytics course that will teach supervised and unsupervised learning strategies, including regression, generalized linear models, regularization, dimension reduction methods, tree-based methods for classification, and clustering. Upper-level analytical methods are shown in practice: e.g., neural networks and Gaussian processes.
We will be using statistical software in this class. You are welcome to use the software of your choice, but class demonstrations will be in R. All help with software in office hours will be limited to R. Please install R and R Studio as soon as possible. R code supporting the JWHT book can be found on the book’s webpage. Matlab code supporting the Bishop book can be downloaded from http://prml.github.io/.
There will be semi-weekly pop quizzes given in class which are entirely extra (exam) credit. Each one, of about ten, will be worth 10 exam points. Each exam is worth 100 points, so if you ace all of the quizzes you easily “make up” for a low on one of the exams.
Grading will nominally follow the typical breakdown on a total percentage scale, e.g., [93-100 A), [90-93 A-), [87-90 B+), [83-87 B), etc. All grades in Canvas will follow this scheme. However the instructor reserves the right to apply a final curve in the students’ favor.
The Undergraduate Honor Code pledge that each member of the university community agrees to abide by states:
“As a Hokie, I will conduct myself with honor and integrity at all times. I will not lie, cheat, or steal, nor will I accept the actions of those who do.” Students enrolled in this course are responsible for abiding by the Honor Code. A student who has doubts about how the Honor Code applies to any assignment is responsible for obtaining specific guidance from the course instructor before submitting the assignment for evaluation. Ignorance of the rules does not exclude any member of the University community from the requirements and expectations of the Honor Code.
For additional information about the Honor Code, please visit: https://www.honorsystem.vt.edu/
The Virginia Tech honor code pledge for assignments is as follows: “I have neither given nor received unauthorized assistance on this assignment.” I will not require you to paste that on your assignment, because that creates a logistical hassle when students forget. Nevertheless, that pledge is applied automatically. The honor code states that “In the absence of a written honor pledge, the Honor Code still applies to an assignment.”
Any student who feels that he or she may need an accommodation because of a disability (learning disability, attention deficit disorder, psychological, physical, etc.), please make an appointment to see me during office hours.