# FIRE Capital One Machine Learning Logo


FIRE Capital One Machine Learning Poster

FIRE Capital One Machine Learning is a part of the FIRE: First-year Innovation & Research Experience (opens new window) Discovery-Based Learning Pilot Program that provides a CURE: Course-based Undergraduate Research Experience (opens new window) 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.

# Research Educator

Dr. Raymond H. Tu

# Peer Mentors

2020
Derek Zhang
Rung-Chuan (Joshua) Lo
Jessica Qin
Richard Gao
Vladimir Leung
Siyuan Peng
2019
Timothy Lin
Derek Zhang
Rung-Chuan (Joshua) Lo
Jessica Qin

# Students & Alumni

2020 2020 Students
2019 2019 Students
2018 2018 Students

# Projects

2020
  • Object Detection In Aerial Images
  • Protein Structure Prediction
  • Speech Recognition
  • Image Stylization
  • Image Super-Resolution
  • Human Pose Estimation
  • Medical Object Detection
  • Text Generation
  • Face Generation
  • Game Playing
  • Object Tracking
  • 3D Object Detection
2019
  • 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
2018
  • 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

# Syllabi

2020
2019
2018

# Description

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.

FIRE Capital One Machine Learning Timeline

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.

# Learning Outcomes

Upon completion of this stream experience, students will be able to:

  • 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.
  • Analyze state-of-the-art techniques from recent scholarly papers and code 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.
  • Lead and drive machine learning projects.
  • Work with machine learning tools, libraries, and modules.
  • Download, manage, and preprocess large datasets.
  • Design, build, and train neural networks for applications such as computer vision, natural language processing, data analytics, or automation.
  • Fine-tune and optimize the model for real-world applications.
  • Critically evaluate findings on the implementation results.
  • Communicate scientific ideas and findings through reports, data visualizations, and presentations.

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 (opens new window), such as becoming an accredited AMP peer mentor (opens new window), be hired (opens new window) at a company and startup, be recruited as a student researcher (opens new window), or be admitted as a graduate student (opens new window) at our university or beyond.

# Posters

# Object Detection

3D Object Detection and Localization Muliple Object Segmentation Nuscenes 3D Detection

# Object Tracking

Object Tracking

# Image Compression

Image Compression

# Audio Classification

Bat Call Identification

# Acknowledgements

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.