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 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

Raymond H. Tu

Peer Mentors

Timothy Lin
Derek Zhang
Rung-Chuan (Joshua) Lo
Jessica Qin


2019 Student Photo


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.

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 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.

Spring Semester

WeekDateOnline ModulesAssignmentsClass Topics
11/30/2019Introduction to FIRE Capital One Machine Learning
22/6/2019OMC1 Introduction to Machine Learning
OMC2 Introduction to deep learning
ASN1 Introduction to PythonSupervised Learning & Linear Regression
32/13/2019OMC3 Linear Regression with Multiple Variables
OMC4 Logistic Regression
ASN2 Supervised Learning with scikit-learnNeural Network Basics
42/20/2019OMC5 Neural Networks Basics
OMC6 Shallow neural networks
ASN3 Deep Learning in PythonDeep Learning Training Techniques
52/27/2019OMC7 DeepNeural Networks
OMC8 Foundations of Convolutional Neural Networks
ASN4 Data Classification ChallengeConvolutional Neural Network Basics
63/6/2019OMC9 Practical aspects of Deep Learning
OMC10 Deep convolutional models: case studies
ASN5 Convolutional Neural Networks for Image ProcessingDeep Convolutional Neural Networks
73/13/2019OMC11 Object detection
OMC12 Special applications: Face recognition & Neural style transfer
ASN6 Image Segmentation Challenge
ASN7 In-Lab Hours I
ASN8 Team Meetings I
Object Segmentation & Similarity Learning
93/27/2019OMC13 ML Strategy (1)
OMC14 ML Strategy (2)
Instance Segmentation & Similarity Learning
104/3/2019OMC15 Optimization algorithms
OMC16 Hyperparameter tuning, Batch Normalization and Programming Frameworks
ASN9 Team Project ProposalSimilarity Learning & Re-Identification
114/10/2019OMC17 Recurrent Neural Networks
OMC18 Natural Language Processing & Word Embeddings
ASN10 Team Project SetupsRecurrent Neural Network Basics
124/17/2019ASN11 Image Segmentation Challenge V2Long Short Term Memory & Word Vector Representation Basics
134/24/2019Word Embedding Techniques
145/1/2019ASN12 Team Project Milestone Paper
ASN13 Team Project Code Presentation
Team Project Code Presentations
155/8/2019ASN14 Peer Review
ASN15 In-Lab Hours II
ASN16 Team Meetings II
ASN17 FIRE Survey
In-Class Competition: Dog vs Cat Image 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.