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Prerrequisites: Any one of the following courses: Machine Learning, Computational Visual Recognition, Natural Language Processing. In summary, this should not be your introductory course to Machine Learning. This will instead be a research-oriented course targeted to PhD Students and MS students with research interests. If you think you have taken a course with a strong Machine Learning component let me know so I can better advise you.
Additional Requirement: In this class you will need a GPU to execute the code in some of your assignments and to develop a course project which will be an important part of your grade. Therefore you need access ideally to a GTX 1080 GPU, or better graphics card, at the minimum you should have access to a CUDA-capable card with at least 4GB of GPU RAM. Additionally, you can fulfill this requirement by learning how to use Amazon EC2 Instances, G2 and P2 instances have GPUs, more info here. As a student, you can get a limited number of free computing credits from the AWS Educate program, however you have to be careful about not running out of your budget using these cloud instances. Note: The CS Department also has three machines named cuda1-3 which you can SSH from any CS server and has five machines with NVIDIA K20's (artemis1-5) in a cluster (instructions). These nodes might be enough for some assignments but probably not for your project. You are encouraged to use them but they do not fullfil the requirement outlined here.
Grading: 50pts (Project) + 30pts (Labs) + 10pts (Class presentation / participation) + 10pts (paper summaries)