Spring 2022 - CMPT 727 G100
Statistical Machine Learning (3)
Class Number: 5565
Delivery Method: In Person
Statistical foundation for machine learning algorithms, emphasizing bias-variance tradeoff. Students will learn principles for choosing effective methods and tailoring them to fit a given learning problem. Potential topics include probabilistic graphical models, maximum likelihood estimation, latent variables and the EM algorithm, convex optimization, and variational and sampling-based methods.
Why we're offering this course: Machine learning now plays a central role in hundreds of fields. All learning methods have common underpinnings based on probability and statistics, but these are not widely understood. This course aims to give students a probabilistic foundation for machine learning and an understanding of probabilistic machine learning methods. ------------------- After you complete this course, you will be able to: * Design and implement probabilistic machine learning techniques including: probabilistic graphical models, discrete and continuous distributions, maximum likelihood estimation, EM algorithm inference, sampling-based inference methods, MCMC, variational inference. * Understand concepts such as: prior, posterior, likelihood, overfitting, bias-variance tradeoff, likelihood, regularization. * Choose between probabilistic machine learning methods, understand what aspects of a data set influence machine learning performance. and foresee which will perform best ------------------- Who should take this course? This course is intended for graduate students with an interest in machine learning or big data. You should take this course if: * You intend to use or develop machine learning in your research or work. * You are interested in fields where probabilisitic machine learning is important including: vision, natural language processing/understanding, medical imaging, robotics, smart cities. * You want to understand statistical behavior of machine learning methods at a deep level. ------------------- Prerequisites: No official prerequisites. However, the course assumes basic knowledge of machine learning (e.g. CMPT 726), probability (e.g. STAT 270) and linear algebra (e.g. MATH 240). The course is open to advanced undergraduates with permission.
- Probabilistic graphical models
- Discrete and continuous distributions
- Maximum likelihood estimation
- EM algorithm
- Bayesian probability
Grading will be based on written and coding assignments and participation in group problem solving sessions.
Graduate Studies Notes:
Important dates and deadlines for graduate students are found here: http://www.sfu.ca/dean-gradstudies/current/important_dates/guidelines.html. The deadline to drop a course with a 100% refund is the end of week 2. The deadline to drop with no notation on your transcript is the end of week 3.
ACADEMIC INTEGRITY: YOUR WORK, YOUR SUCCESS
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Each student is responsible for his or her conduct as it affects the University community. Academic dishonesty, in whatever form, is ultimately destructive of the values of the University. Furthermore, it is unfair and discouraging to the majority of students who pursue their studies honestly. Scholarly integrity is required of all members of the University. http://www.sfu.ca/policies/gazette/student/s10-01.html
TEACHING AT SFU IN SPRING 2022
Teaching at SFU in spring 2022 will involve primarily in-person instruction, with safety plans in place. Some courses will still be offered through remote methods, and if so, this will be clearly identified in the schedule of classes. You will also know at enrollment whether remote course components will be “live” (synchronous) or at your own pace (asynchronous).
Enrolling in a course acknowledges that you are able to attend in whatever format is required. You should not enroll in a course that is in-person if you are not able to return to campus, and should be aware that remote study may entail different modes of learning, interaction with your instructor, and ways of getting feedback on your work than may be the case for in-person classes.
Students with hidden or visible disabilities who may need class or exam accommodations, including in the context of remote learning, are advised to register with the SFU Centre for Accessible Learning (email@example.com or 778-782-3112) as early as possible in order to prepare for the spring 2022 term.