Deep Learning is a type of Artificial Intelligence where we give the computer the ability to learn, rather than tell it what to learn. Here at MPCR, we look at Deep Learning as a member of multiple fields, if not every field. AI has its roots in Psychology and Biology, and we strive to remain true to those origins when we consider Deep Learning as a Theory of the Brain. However, it is also highly computational and is an important tool for today’s Data Scientists. Deep Learning has already begun to answer questions in fields such as Medicine, Biology, Chemistry, and Engineering, and it is gaining momentum. Understanding and contributing to the Deep Learning Movement in AI will have an impact both on the researcher, as well as society on the whole.
We are putting these artificial brains to work in diagnosing cancer, finding potential new drugs to treat HIV, learning how to smell, and driving autonomously. These projects come from different fields, but converge on their approach: Deep Learning. MPCR is equipped with multiple Graphics Processing Units (GPUs) to run various types of Artificial Neural Networks (ANNs) on our data. With these resources, we firmly believe that Deep Learning will revolutionize many fields of science and engineering, allowing for new ways of analyzing data and generating new questions and methodologies. In this short course, we strive to provide participants with the skills to bring Deep Learning to their unique and diverse problems from any field.
The goal of this course is to take someone with minimal or no prior experience in Deep Learning and give them both the conceptual framework to understand the algorithms, as well as the computational practice to be able to begin to deploy them in their home research settings.
Participants will gain hands-on experience of topics using Python via Colaboratory on Google Drive. They will also be introduced to some of the applied research projects currently taking place in the lab to see how Deep Learning can be applied to a wide range of problems. Students do not need any previous experience with Python or computer programming but will need to bring their own laptop computers to access the scripts online.
We will be covering four major topics:
The day-to-day schedule will consist of 10am to 4pm of content, with conceptual talks in the mornings and hands-on practice in the afternoons. Social events will be planned for the evenings, such as a BBQ on the beach.
This short course is open to students that are interested in machine learning, and particularly deep learning. Faculty that are interested in attending are also welcome, but please email email@example.com for the registration process. Students in high school, undergraduate, or graduate school are welcome to apply. Registration cost is $250 to cover meals and incidentals. A limited number of competitive scholarships are available by application. Students must bring their own laptop computers with WiFi capabilities.
Registration fee goes towards covering lunches on Tuesday, Wednesday, Thursday, and Friday; dinners on Tuesday and Wednesday; and course materials.
The application deadline is May 7th, 2018. There is a basic application, with supplemental materials if applying for a scholarship. Participants will be notified of decisions by May 14th and payment must be made by June 1st. The application consists of:
There are a limited number of spots for students. Scholarships will be awarded based on merit, but will take into consideration other expenses such as travel and lodging.
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