Student Resources
Academic Standards & Integrity

Academic integrity is fundamental to every facet of the scholarly process and is expected of every student in the Master of Science in Machine Learning and Data Science program in all academic undertakings. Integrity includes strict adherence to academic honesty, and to ethical conduct consistent with standards that respect the intellectual efforts of both oneself and others.

Ensuring integrity in academic work is a joint enterprise involving both faculty and students. Among the most important goals of graduate education are maintaining an environment of academic integrity and instilling in students a lifelong commitment to the academic honesty that is fundamental to good scholarship. These goals are best achieved as a result of effective dialogue between students and faculty mentors regarding academic integrity, and by the examples of members of the academic community whose intellectual accomplishments demonstrate sensitivity to the nuances of ethical conduct in scholarly work.

Standards of academic honesty are violated whenever a student engages in any action that jeopardizes the integrity of scholarly work. Such actions include:

  • Cheating in the classroom or on examinations
  • The intentional and deliberate misuse of data in order to draw conclusions that may not be warranted by the evidence
  • Fabrication of data
  • Omission or concealment of conflicting data for the purpose of misleading others
  • Use of another's words, ideas or, creative productions without citation
  • Paraphrasing or summarizing another's material in such a way as to misrepresent the author's intentions
  • Use of privileged material or unpublished work without permission

Academic dishonesty is a serious matter, and it will be adjudicated in accordance with procedures approved by the McCormick School of Engineering and Applied Science.