FIRE SEMESTER 2: FIRE198-0112

Spring 2020

See our Post COVID-19 operations plan here.

Stream and Research Educator Availability

Instructor:

Dr. Raymond Tu

Class Location:

ATL2339

Email:

hh2@umd.edu

Class Time:

Wed 2-2:50pm

Office Location:

AVW3136

Lab Location:

AVW3136

Office Hours:

See Lab hours

Lab Hours:

Mon 12-5pm, Tue-Fri 9am-5pm

Peer Mentors:

Derek Zhang

Jessica Qin

Richard Gao

Rung-Chuan (Joshua) Lo

Siyuan Peng

Vladimir Leung

Course Description

This course is the second in the sequence of three courses you will complete with the First-Year Innovation & Research Experience (FIRE) program. Through this course, you will be welcomed into your stream’s discipline, receive hands-on training in authentic research methods, and begin to take ownership of part of the stream’s research agenda as part of your development towards fearless career readiness.

Capital One Machine Learning is a research project-based class that spans over two semesters. This course will focus on the concepts related to the process of independent research, including collaboration with peers, communication of ideas, troubleshooting unexpected outcomes, and discipline-specific methodologies. Scheduled class meetings will focus on the discussion of primary literature, troubleshooting research issues, and continual review of individual and group research progress. Due to the unforeseen change in the FIRE research engagement, all students will be granted full credit for the in-lab research hours requirement. Research sessions in the lab will focus on training in current discipline-specific methods and practices, as well as giving students relevant experiences that seek to build resiliency and critical analysis skills.

Course Outcomes

At the completion of the course you will be able to:

Required Books & Reading

All course materials will be posted to ELMS.

Course Policies

Please refer to the following website for all course policies:

http://www.ugst.umd.edu/courserelatedpolicies.html

A complete download is also available:

https://www.ugst.umd.edu/documents/CourseRelatedPolicies.pdf

As stated on this website:

The University expects each student to take full responsibility for their academic work and academic progress.

As a student you have the responsibility to be familiar with and uphold the Code of Academic Integrity and the Code of Conduct, as well as for notifying your course instructors in a timely fashion regarding academic accommodations related to absences and accessibility.

Course Evaluation

Student learning will be evaluated by 14 assessments (as further detailed in the Course Schedule).  The course instructor will provide details on how each assessment will be evaluated for a final grade.

ASN

Description

% of grade

1

Setting Long-Term Goals & Self-Commitment

2.5

2

Testing MNIST Digits Classification with Real Handwriting

5

3

Testing CIFAR10 Image Classification with Real Photos

5

4

Testing CamVid Semantic Segmentation with Real Photos

5

5

Testing LFW Face Recognition Preprocessor with Real Photos

5

6

Testing LFW Face Recognition with Real Photos

5

7

Testing Open Images Object Detection with Real Photos

10

8

Team Contract

2.5

9

Fall Semester Project Preparation

10

10

Facial Recognition Team Project

25

11

Team Member Evaluation

5

12

Class Participation

5

13

Lab Attendance

10

14

FIRE Career Readiness

5

Due to the unforeseen change in the FIRE research engagement, all students will be granted full credit for the in-lab research hours requirement.

Final Letter Grade Determination

This course uses plus and minus grading following the scale provided below.  Final grades will be rounded to the nearest number. (Example: a final grade of 89.5 will be rounded up to 90. A final course grade of 89.4 will remain 89.4.)

Final letter grades will be determined using the following guidelines:

+

97.0%

+

87.0%

+

77.0%

+

67.0%

A

93.0%

B

83.0%

C

73.0%

D

63.0%

F

< 60.0%

-

90.0%

-

80.0%

-

70.0%

-

60.0%

Course Schedule

This schedule is subject to change as the semester progresses.

Week

Prerequisite

Class Activity

Lab Activity

Assignment(s)

1

Intro to FIRE Capital One Machine Learning

Review learning materials & complete assignments

- Setting Long-Term Goals & Self-Commitment

2

Install Packages for MNIST Digits Classification Project

MNIST Handwritten Digit Classification

Review learning materials & complete assignments

- Testing MNIST Digits Classification with Real Handwriting

3

Installing Packages for CIFAR10 Image Classification Project

CIFAR10 Image Classification

Review learning materials & complete assignments

- Testing CIFAR10 Image Classification with Real Photos

4

Installing Packages for CamVid Semantic Segmentation Project

CamVid Semantic Segmentation

Review learning materials & complete assignments

- Testing CamVid Semantic Segmentation with Real Photos

5

Installing Packages for LFW Face Recognition Project

LFW Face Recognition Preprocessor

Review learning materials & complete assignments

- Testing LFW Face Recognition Preprocessor with Real Photos

6

Installing Additional Packages for LFW Face Recognition Project

LFW Face Recognition

Review learning materials & complete assignments

- Testing LFW Face Recognition with Real Photos

7

Open Images Object Detection Preprocessor

Open Images Object Detection Prerequisite

Review learning materials & complete assignments

- Testing Open Images Object Detection with Real Photos

- Class Participation I

- Lab Attendance I

8

3/16-3/20

SPRING BREAK

9

3/23-3/27

EXTENDED CLOSURE

10

3/30-4/3

Open Images Object Detection

Review learning materials & complete assignments

- Team Contract

11

4/6-
4/10

Team formation

Intro to Facial Recognition Team Project

Intro to COML Fall Semester Projects

Work on team project & assignments

- Fall Semester Project Prep: Dataset Review

12

Facial Recognition Team Project Review

Fall Semester Projects Dataset/Paper/Code Review

Work on team project & assignments

- Team Member Evaluation I

- Fall Semester Project Prep: Paper Review

13

Facial Recognition Team Project Review

Fall Semester Projects Dataset/Paper/Code Review

Work on team project & assignments

- Fall Semester Project Prep: Code Review

14

FIRE Career Readiness

FIRE Survey

Work on team project & assignments

- FIRE Career Readiness

15

Facial Recognition Team Project Demo

Complete final assignments

- Team Member Evaluation II

- Class Participation II

- Lab Attendance II

- Facial Recognition Team Project: Live Demo

- Facial Recognition Team Project: Final Code

END OF COURSE