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About the course
Machine Learning is the study of computer algorithms that improve automatically through experience. It is one of the most exciting aspects of artificial intelligence, and is the basis for many of its industrial applications. It is the preferred framework for many applications, such as face detection (auto-focus in your digital camera), hand-written digit recognition, speech recognition, and credit card fraud detection.
This course is a 700-level grad course - it will be a lecture-style course and be taught from a textbook (this will be familiar for undergrads taking it as CMPT 419). This course will start from the basics, no prior experience in machine learning nor pattern recognition will be presumed. Students will gain hands-on experience with state of the art machine learning algorithms via programming assignments on real datasets and a course project.
Prerequisite
The most important prerequisite for this course is a strong mathematics background. If you are an undergrad, you should have completed your CS major requirements: MATH 151, 152, 232, and STAT 270 and be comfortable with these concepts. If you are a grad student, you should have equivalent background knowledge. It will be possible to refresh your knowledge at the beginning of the course, but basically I don't want anyone to run from the room screaming if I say "eigenvector" or "covariance matrix". If you have questions about your readiness for this course, please ask.
Assignments
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Assignments and grading:
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Programming assignments will be done in MATLAB. MATLAB is easy to use. It also provides many tools for numerical computation which are useful for implementing machine learning techniques. Students who are not familiar with MATLAB are expected to acquire basic proficiency. The following resources are helpful for learning MATLAB: