The course introduces students to the design of algorithms that enable machines to "learn". In contrast to the classic paradigm where machines are programmed by specifying a set of instructions that dictate what exactly a machine should do, a new paradigm is developed whereby machines are presented with examples from which they learn what to do. This is especially useful in complex tasks such as natural language processing, information retrieval, data mining, computer vision and robotics where it is not practical for a programmer to enumerate all possible situations in order to specify suitable instructions for all situations. Instead, a machine is fed with large datasets of examples from which it automatically learns suitable rules to follow. The course will introduce the basics of machine learning and data analysis.
There will be a total of 5 assignments in this course, which helps students develop conceptual understanding of the machine learning techniques, as well as technical skills in implementing and comparing learning algorithms. Each teaching assistant is responsible for a particular assignment, including holding a tutorial session for the assignment, answering questions on Piazza, as well as grading the assignment. See the assignments page for more details.
There is a project component to this course, which is required for CS680 students, but optional for CS480 students. The project requires four deliverables: a 1-page proposal, a 4.5-page milestone report, a poster presentation, and a 8-page project report. See the project page for more details.
There will also be a midterm and final exam.
Participation is worth 5%, and is based on completion of all assignments by due date (1%), in-class attendance and participation (1%), and participation as a peer reviewer of project posters (3%).
|CS 480||CS 680|
|5 Assignments||35% (7% each)||25% (5% each)|
|Project||5% bonus (optional)||25% (required)|
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