Intermediate Data Analytics and Machine Learning


  • Course Number: CMDA/CS/STAT 4654
  • Institution: Virginia Tech
  • Term, Dates, Location: Spring Semester 2017, M/W 4:00-5:15 SEITZ 313
  • Instructor: Robert B. Gramacy (;
    • Office Hours & Location: M & Tu 10-11am, Hutcheson 403G
  • Grader: Yi Liu (
    • Office Hours & Location: Grader will not hold office hours
  • Prerequisites: CMDA 2006 or equivalent. Particularly intermediate linear algebra, probability, basic statistics (standard errors), regression. (Basically, this can’t be your first course in stats!)
  • Required text: Pattern Recognition and Machine Learning, Cristopher M. Bishop. Google the title and you will find a free pdf.
  • Optional text: Machine Learning: a Probabilistic Perspective, Kevin Murphy.
  • Web: Course materials will be provided at; Canvas will only be used for grading purposes

About the course


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., advanced naïve Bayes, 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. MATLAB code supporting the book can be downloaded from

Grading details


  • 40% Homework
  • 20% Exam #1, tentatively Monday February 27
  • 20% Exam #2, tentatively Wednesday April 12
  • 20% Take Home Final Exam (cumulative), Due 8 May 2017 11:59pm

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.


  • There will be two written exams and a cumulative take home final exam. All in-class exams will be closed book and closed notes (with the exception of a cheat sheet). The take home final will be open book/notes, however students may not collaborate with anyone else. What is turned in must be entirely their own work.
  • During exams, students should be considerate of their classmates by arriving on time. If a student arrives after at least one student has finished the exam and left the room, he/she will NOT be allowed to take the exam and will receive a grade of zero. Cell phones should be turned off before entering the exam room.
  • Make-up exams will be offered for students who have well-documented emergencies approved by the instructor or reported in advance.


  • Homework will be assigned and due on a regular basis. Students are welcome to collaborate with one another, but are required to submit their own work as well as be able to reproduce it.
  • All work must be shown and software must be used when appropriate with attached software output.
  • Late homework will not be accepted unless previously approved by the instructor.
  • All the homework grades will be kept which means NO homework grade will be dropped.


Honor code

The Virginia Tech Honor Code will be strictly enforced in this course. All graded assignments must be composed of your own work.

Services for students with disabilities

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.

Important dates

  • Please take note of the important dates and deadlines noted on the Registrar’s web page for Spring 2017.
  • Please take note of the dates of the two exams, and of the due date of the take home final exam (May 8) listed above.
  • There are no planned cancellations of class at this time.