The doctoral degree in Machine Learning explores the ways in which algorithmic data is generated and leveraged for statistical applications and computational analysis in model-based decision-making. Students will learn the current operations, international relationships, and areas of improvement in this field, as well as research methodologies and future demands of the industry.
The PhD in Machine Learning is for current or experienced professionals in a field related to machine learning, artificial intelligence, computer science, or data analytics. Students will pursue a deep proficiency in this area using interdisciplinary methodologies, cutting-edge courses, and dynamic faculty. Graduates will contribute significantly to the Machine Learning field through the creation of new knowledge and ideas, and will quickly develop the skills to engage in leadership, research, and publishing.
As your PhD progresses, you will move through a series of progression points and review stages by your academic supervisor. This ensures that you are engaged in research that will lead to the production of a high-quality thesis and/or publications, and that you are on track to complete this in the time available. Following submission of your PhD Thesis or accepted three academic journal articles, you will have an oral presentation assessed by an external expert in your field.
Student Outcomes
Upon graduation, graduates will be able to
- Graduates will integrate the theoretical basis and practical applications of Machine Learning into their professional work.
- Graduates will demonstrate the highest mastery of the subject matter.
- Graduates will evaluate complex problems, synthesize divergent/alternative/ contradictory perspectives and ideas fully, and develop advanced solutions to Machine Learning challenges.
- Graduates will contribute to the body of knowledge in the study of the subject.
- Graduates will be at the forefront of Machine Learning planning and implementation.
Return to: