Statistical Machine Learning (M.Sc. or Ph.D.)
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Overview
The Master of Science (M.Sc.) and Doctor of Philosophy (Ph.D.) degrees in Statistical Machine Learning may be taken jointly in the Department of Computing Science and in the Department of Mathematical and Statistical Sciences. The program emphasizes the theoretical aspects of the design and analysis of machine learning algorithms using tools of statistics and computer science.
Students can apply either to the Department of Computing Science or to the Department of Mathematical and Statistical Sciences to participate in this program. The department the student applied to becomes the host department of the student, gives his/her degree and does the administration of the program.
Why take Statistical Machine Learning?
If you are a Computing Science student interested in machine learning, why should you take the SML program? What are the benefits? And what are the pitfalls?
90 percent of machine learning is based on statistical ideas. Statistical ideas and statistical thinking constitute the core of the subject. If you really want to understand topics such as overfitting, cross validation and its uses, the limits of learnability, adaptive methods, why is LASSO a good idea (if at all), then the SML program can help you speak this language.
The SML program gives you the opportunity to build strong foundations in probability theory and statistics. These days, the boundary between machine learning and statistics is even less clear than it was ever before. Statisticians publish in machine learning journals and at machine learning conferences and vice versa. After all, both paths are exploring better ways to create better models which would, in turn, produce better predictions. In fact, the demand for rigorous analysis of algorithms is bigger than ever  and for good reason: a solid understanding of algorithms is necessary to build a good foundation so that the tower of results built on top of it does not collapse. Empirical evidence is important, but it can never tell the whole story.
What if you are a Statistics student? Why should you care?
Machine learning is a very vibrant and rapidly expanding part of statistics. As new models appear, so do the opportunities. Scientists in machine learning like nonstandard models and situations creating many wonderful research opportunities. Also, being a new subject, it may be easier to gain recognition from the community (though in truth, you'll still have to work hard at it!)
What are the job prospects like? Will this program enhance your chances of employment?
These days, employers (looking for machine learning researchers) are aware that machine learning and statistics are tightly intervowen. Having an SML degree certifying that you speak both languages is to your advantage. You are also given double the options as you apply for jobs  you can look into jobs that require a computing science/machine learning background as well as a statistics/probability theory background. If you decide to stay in the academia, you can consider one of the many openings are in statistics. Or, your specialization in machine learning may lead you to work as a researcher for Google, Yahoo, Amazon or Netflix.
Who should not take the SML program?
If you are a computing science student who is bored of theory, math, and probability, do not take the program. If you are afraid of hard work, this program is not for you! In fact, the load for this program is slightly higher than average.
M.Sc. Program in SML
M.Sc. Entrance Requirements
The entrance requirement for the Master of Science degree in Statistical Machine Learning is a fouryear degree in Computing Science or in Mathematical and Statistical Sciences with a GPA of 3.0 or better in the last two years of study, or an equivalent qualification from a recognized institution.
M.Sc. Course Requirements
The M.Sc. degree can be obtained only in a thesisbased program. Computing Science M.Sc. students participating in the program need to take:
 Five graduate courses (at the 500level or higher) from a list of approved courses

 At least one out of these five must be through the Mathematical and Statistical Sciences department
 At least one must be through the Computing Science department
 Normally two out of five must be at the 600level
 One course on Teaching and Research Methods (CMPUT 603)
 A thesis is required in an area associated with Statistical Machine Learning
M.Sc. Approved Courses
Mathematics and Statistical Sciences courses
 STAT 501505: Directed Study
 STAT 335: Statistical Quality Control and Industrial Statistics
 STAT 361/504: Sampling Techniques
 STAT 368/501: Introduction to the Design and Analysis of Experiments
 STAT 378/502: Applied Regression Analysis
 STAT 432: Survival Analysis
 STAT 441/505: Applied Statistical Methods for Data Mining
 STAT 471: Probability I
 STAT 479/503: Time Series Analysis
 STAT 512: Techniques of Mathematics for Statistics
 STAT 532: Survival Analysis
 STAT 562: Discrete Data Analysis
 STAT 568: Design and Analysis of Experiments
 STAT 575: Multivariate Statistical Analysis
 STAT 578: Regression Analysis
 STAT 580: Stochastic Processes
The list of offered courses varies from year to year. See the graduate course directory for this year's list of approved courses.
Ph.D. Program in SML
Ph.D. Course Requirements
Computing Science Ph.D. students participating in this program need to take:
 Four courses from the approved list of courses

 At least two out of four from Mathematics & Statistical Sciences
 At least two out of four from Computing Science
 One course on Teaching and Research Methods (CMPUT 603)
Ph.D. Approved Courses
For completing course requirements, select courses from the following (note, courses may not all be offered each year).
Mathematics and Statistical Sciences courses
 STAT 512: Techniques of Mathematics for Statistics
 STAT 566: Methods of Statistical Inference (or STAT 664, Advanced Statistical Inference)
 STAT 571: Probability and Measure
 STAT 575: Multivariate Analysis
 STAT 580: Stochastic Processes
 STAT 665: Asymptotic Methods in Statistical Inference
 STAT 671: Probability Theory I
 STAT 672: Probability Theory II
 STAT 503: Directed Study III
 STAT 679: Time Series Analysis
 STAT 578: Regression Analysis
The list of offered courses varies from year to year. See the graduate course directory for this year's list of approved courses.
How to Apply
To express interest in the Statistical Machine Learning program, follow the departmental application process.
 Masters students are admitted into the mainstream Masters program but can apply to transfer to the Statistical Machine Learning program if a faculty member agrees to supervision and the thesis will be in an area related to statistical machine learning. This happens after the student arrives on campus and discusses this option in person with the potential supervisor.
 Ph.D. students are accepted into the SML program at admission time or, with their supervisor's support, can apply to transfer to the Statistical Machine Learning program if their thesis will be in an area related to statistical machine learning.