Doctor of Philosophy in Computing Science with Specialization in Statistical Machine Learning (Computing Science)

Statistical Machine Learning emphasizes the theoretical aspects of the design and analysis of machine learning algorithms using tools of statistics and computer science.

Students can alternately apply to the Department of Mathematical and Statistical Sciences to participate in this program.

Program Requirements

Students are required to complete 15 units in coursework and a thesis.

Coursework (15 units)

Courses are to be chosen in consultation with the supervisor and approved by the Associate Chair (Graduate). Courses must include:

Four 3-unit graduate level courses approved for this specialization
- Two 3-unit courses must be through the Department of Mathematical and Statistical Sciences
- Two 3-unit courses must be through the Department of Computing Science
CMPUT 603 (3 units) - Teaching and Research Methods

Thesis

Registration in 900-level THES. The thesis must be in an area associated with Statistical Machine Learning. The thesis topic is to be approved by the supervisor.

Candidacy Exam

Students are required to write a candidacy document and take a candidacy oral examination. Students must demonstrate that they have adequate knowledge of the discipline and subject matter relevant to the thesis topic and the ability to pursue and complete original research at an advanced level.

Academic Standing Requirement

PhD students are required to maintain a minimum GPA of 3.3 in three graduate courses (excluding CMPUT 603) with no grade lower than B-.

Residence Requirement

The minimum period of residence is two academic years of full-time attendance at the University of Alberta.

Length of Program

Students are encouraged to finish their PhD program within 5 years.

The maximum time to complete the PhD program as set by the Faculty of Graduate & Postdoctoral Studies is six years.