Students are expected to adhere to the Duke Community Standard. If a student is responsible for academic dishonesty on a graded item in this course, then the student will have an opportunity to admit the infraction and, if approved by the Office of Student Conduct, resolve it directly through a faculty-student resolution agreement; the terms of that agreement would then dictate the consequences. If the student is found responsible through the Office of Student Conduct and the infraction is not resolved by a faculty-student resolution agreement, then the student will receive a failing (unsatisfactory) grade for the final grade in the course.

  • Students may work on programming assignments with a maximum of one (1) other individual in the class. However, both individuals should contribute equally to the assignment and understand all parts of the code written.
  • Students are expected to write their adherence to the Duke Community Standard in a README for every assignment. Students are allowed to consult others outside of their group—limited to Duke students and faculty—about the assignment only in a general way, but not actually provide/receive code to/from other students. If assistance is received from other individuals (excluding the instructors), it should be cited in the README. Students should be prepared to explain any program code they submit.
  • It is acceptable to use small pieces of outside code (found on the Internet or otherwise) due to the nature of this course—but not entire methods or programs. Using open source libraries and packages is allowed. If you are concerned whether using a piece of code is within the Duke Community Standard, please ask. All code used should be properly cited.
  • All submissions are subject to automated plagiarism detection. Assignments will be randomly checked using the MOSS Plagiarism Detector.

Programming Assignment #1: Regression & Model Evaluation (due October 3) [dataset]

Programming Assignment #2: Classification & Dimensionality Reduction (due October 31) [dataset]

Programming Assignment #3: Applied Machine Learning (due December 5)