Pattern recognition and analysis
Master
In Maynard (USA)
Description
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Type
Master
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Location
Maynard (USA)
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Start date
Different dates available
This class deals with the fundamentals of characterizing and recognizing patterns and features of interest in numerical data. We discuss the basic tools and theory for signal understanding problems with applications to user modeling, affect recognition, speech recognition and understanding, computer vision, physiological analysis, and more. We also cover decision theory, statistical classification, maximum likelihood and Bayesian estimation, nonparametric methods, unsupervised learning and clustering. Additional topics on machine and human learning from active research are also talked about in the class.
Facilities
Location
Start date
Start date
Reviews
Course programme
Lectures: 2 sessions / week, 1.5 hours / session
Recitations: 1 session / week, 1 hour / session
Homework/Mini-Projects, due every 1-2 weeks up until 3 weeks before the end of the term.
These will involve some programming (MATLAB®) assignments.
Assignments are due by the start of class on the due date. If you are late, you will get a zero on the assignment. However, the lowest assignment grade will be dropped in computing the final grade.
The goal of the assignments is to help you learn, not to see how many points you can get. Grades in graduate school do not matter as much as in undergraduate: what you learn is what matters. Thus, if you stumble across old course material with similar-looking problems, please try not to look at their solutions, but rather work the problem(s) yourself. Start early, and don't be disappointed if you get stuck when you try to do it solo; that frustrating experience can lead to more memorable and effective learning. Please feel free to come to the staff for help, and also to collaborate on the problems and projects with each other. Collaboration should be at the "whiteboard" level: discuss ideas, techniques, even details - but write your answers independently. This includes writing MATLAB® code independently, and not copying code or solutions from each other or from similar problems from previous years. If you are caught violating this policy it will result in an automatic F for the assignment and may result in an F for your grade for the class. (This has happened to people before - it is not an empty threat.) If you team up on the final project (teams of two are encouraged), then you may submit one report which includes a jointly written and signed statement of who did what.
The midterm will be closed-book, but we will allow a cheat sheet.
All students are expected to attend all project presentations the last two days of class; these are very educational experiences, and thus attendance these last two days will contribute to your final grade.
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Pattern recognition and analysis