FIRE Capital One Machine Learning is a part of the FIRE: First-year Innovation & Research Experience Discovery-Based Learning Pilot Program that provides a CURE: Course-based Undergraduate Research Experience that immerses first-year (new freshman, freshman connection, transfer) undergraduate students in state-of-the-art research and faculty-led mentorship experience in machine learning, deep learning, and artificial intelligence. We focus on projects using recently developed techniques, perspectives, and applications for market-relevant areas such as computer vision, natural language processing, and data analytics.
Our recent machine learning projects include:
- 3D Object Detection and Localization for Autonomous Vehicles
- Object Recognition and Tracking for Surveillance Videos
- Extreme Image Compression from Learned Objects
The FIRE Capital One Machine Learning Innovation & Research Stream Experience provides a 2-semester course sequence that spans over a full year, with the first day of the course starting at the Spring semester each year and the last day ending with the Fall semester. Students from all degree majors enroll in our FIRE171/271 course during the Spring and Fall semesters after completing their FIRE Semester 1 (FIRE120) course in the Fall semester.
Other than 1 hour of scheduled class meetings per week, the FIRE Capital One Machine Learning Stream Experience requires each student to commit 6 additional hours of independent and team work each week, most of which will be in the FIRE Capital One Machine Learning lab.
Each student throughout the semester will work both individually and as a team member of a faculty-led machine learning project. Research sessions in the lab setting will focus on faculty-led research, including collaboration with peers, communication of ideas, troubleshooting unexpected outcomes, as well as giving students relevant experiences that seek to build resiliency and critical analysis skills.
Scheduled class meetings will focus on training in current discipline-specific methods and practices, discussion of primary literature, troubleshooting research issues, and continual review of individual and group research progress.
Upon completion of this course, students will be able to:
- Demonstrate knowledge of fundamental concepts and ideas in neural networks and deep learning.
- Demonstrate the capacity to drive machine learning projects.
- Demonstrate proficiency with machine learning tools, programming, and implementation.
- Understand how to preprocess the data for machine learning.
- Demonstrate capacities to work with and manage large datasets.
- Understand how to build neural networks for applications such as computer vision, natural language processing, or data analytics.
- Understand how to fine tune and optimize the model for real-world applications.
- Critically evaluate findings on the implementation results.
- Communicate ideas and research findings through academic writing, data visualizations, and presentations.
During the mid-point of the first-semester (FIRE semester 2) of the FIRE Capital One Machine Learning Innovation & Research Stream Experience, students can apply for the competitive opportunity to become a FIRE summer fellow. As a summer fellow, each students will receive a moderate stipend to take part in an 8-week immersive research experience and commit 12-18 hours per week in the FIRE Capital One Machine Learning lab during the summer period. Each summer fellow will truly be a member of a working research group led by the Research Educator. The summer fellowship program offers an excellent opportunity to rapidly advance the student's research, professional, and leadership skills.
We sincerely thank the following sponsors for providing us with the funds, services, and tools to help FIRE Capital One Machine Learning provide a better innovation & research experience for our students.