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
# Research Educator
# Peer Mentors
- Object Detection in Aerial Image
- Photorealistic Image Stylization
- Protein Structure Prediction
- Muti-Person Pose Estimation
- Voice Speech Recognition
- Image Super Resolution
- 3D Object Detection and Localization of Camera, LIDAR, and RADAR Objects for Self-Driving Cars
- Visual Object Detection and Tracking for Surveillance Videos and Self-Driving Cars
- Extreme Image Compression from Learned Objects for Images and Videos
- Image Caption Generation for Visually Impaired Users
- Instance-level Object Tracking and Segmentation Across Video Frames
- Using Neural Networks to Identify Individual Bats of the Myotis Vivesi Species
- Tracking Bee Identities and Behaviors with Convolutional Neural Networks
- Improving 9-1-1 Call Operations Efficiency with Natural Language Processing
- Detecting Driver Drowsiness and Attentiveness Through Facial Recognition
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 will enroll in our FIRE198 & FIRE298 Section 0112 course sequence 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 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 stream experience, students will be able to:
- Demonstrate sound reasoning to analyze issues, make decisions, and overcome problems (career readiness - critical thinking & problem solving)
- Articulate thoughts and ideas clearly and effectively in written and oral forms (career readiness - oral and written communications).
- Build collaborative relationships representing diverse cultures, races, ages, genders, religions, lifestyles, and viewpoints (career readiness - teamwork & collaboration).
- Demonstrate personal accountability and effective work habits (punctuality, working productively with others, time and workload management - career readiness - professionalism & work ethic).
- Demonstrate knowledge of fundamental concepts and ideas in neural networks and deep learning.
- Understand how to lead machine learning projects.
- Analyze state-of-the-art techniques from recent scholarly papers and open-source repositories.
- Perform data preprocessing, training, optimization, and evaluation of machine learning models using deep learning frameworks (such as Keras, Tensorflow, and PyTorch).
- Collaborate in a teamwork environment with the research leader and a team of student researchers to design, implement, and apply machine learning models for real-world usage.
- 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 scientific ideas through reports, 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 FIRE summer fellowship that enables a research-intensive 8 week summer experience. FIRE summer fellowship students will be required to commit to either 12 hours per week or 18 hours per week. As a summer fellow, each students will receive a moderate stipend for both the 12 and 18 hour weekly commitment options. Stipends are intended to help defray some costs of participation. Each summer fellow will truly be a member of a working research group led by the Research Educator in the FIRE Capital One Machine Learning lab during the summer period. The summer fellowship program offers an excellent opportunity to rapidly advance the student's research, professional, and leadership skills.
Upon the completion of the FIRE Capital One Machine Learning stream experience, students will also have the opportunity to receive recommendation help them succeed in their next steps, such as becoming an accredited AMP peer mentor, be hired at a company and startup, be recruited as a student researcher, or be admitted as a graduate student at our university or beyond.
# Object Detection
# Object Tracking
# Image Compression
# Audio Classification
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.