Doctor of Philosophy in Computer Science - Machine Learning

The David R. Cheriton School of Computer Science has an international reputation in teaching, academics, research, and employment. We attract exceptional students from all over the world to study and conduct research with our award-winning faculty. You can participate in research projects in a wide variety of topics with our internationally acclaimed researchers. Our research spans the field of computer science, from core work on systems, theory and programming languages to human-computer interaction, DNA and quantum computing to theoretical and applied machine learning, just to name a few. As a graduate student, you will: Access research-intensive lab spaces. Gain the opportunity to publish your work in top conferences and journals. Present at premier conferences in front of peers, industry leaders, researchers, and experts in your field. As a graduate student, you will have the independence to pursue your preferred area of research with a faculty If you want to continue pursuing research and expand your learning, you will work with a supervisor to develop a thesis. As a graduate student at the PhD level you will be expected to conduct meaningful research that expands the scope of your graduate work.

Machine learning is an area of specialization of statistics crossed with computer science, most notably with such areas as computational statistics, scientific computation, data visualization and computational complexity. We live in an era where information technologies allow individuals and large organizations to gather increasingly large volumes of data about business transactions, web click traces, health records, etc. This data contains a wealth of information, however, mining the data to extract relevant information is challenging. For instance, how can a fraud be identified from a stream of transactions, how can user preferences be inferred from click traces to improve web services, how can new health indices be designed based on logs of physiological measurements to better assess and monitor chronic diseases Research in machine learning is concerned largely with the analysis and development of algorithms to explore, discover, visualize and model structure in data as well as to make predictions and decisions based on that structure. Motivating data is often incomplete, noisy, nonhomogeneous in structure and large in size (e.g., large number of observations or dimensions, or both). Special attention is paid to the development of computationally efficient (with respect to time and memory usage) data analysis algorithms. Research includes the mathematical and computational analysis of the statistical methodology, the development of new methodologies, algorithms and software, and the application of these to substantive problems from other areas.

A Master's degree in Computer Science with a 78% average.
Student with an undergraduate degree in Computer Science may apply for admission directly to the PhD program. Successful applicants will have an outstanding academic record, breadth of knowledge in computer science, and strong letters of recommendation.
PhD applicants may be admitted into the Master of Mathematics (MMath) program. Like all MMath students, they will have the option to transfer into the PhD program before completing the master's thesis if their performance warrants.
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Supplementary information form (SIF)
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Transcript(s)

References
Three references are required, at least two academic
Proof of English language proficiency, if applicable
TOEFL 93 (writing 22, speaking 22), IELTS 6.5 (writing 6.0, speaking 6.5)