Course Overview
Course Description
Content
This course introduces topics in Deep Learning with an emphasis on individual inquiry, problem-based learning, group work, and computer programming.
Prerequisites
CS 3460 and MAT 2240 with a grade of "C" or higher
Objectives
- Build, train, and evaluate models for a variety of deep learning problems;
- Explore a variety of models that perform well;
- Demonstrate self-guided learning;
- Communicate your findings; and
- Work well with your team.
Course Requirements
Materials
Required
- Jon Krohn, Deep Learning Illustrated
- Can-do attitude!
Resources
Grade Items
Attendance
Unexcused absences deduct 1% from your final grade.
Group Work and Participation
You will receive a grade based on my observations of your team meetings during class. Attendance is mandatory and although excused absences will not directly affect your grade, neglecting your team will. You also earn credit by showing up and submitting quality feedback for your teammates, assessing other teams presentations, and submitting inquiry reports before group meetings.
Model Performance and Exploration
A significant portion of this class will be spent designing, building, training, and evaluating deep learning models. A portion of your grade will be determined by the timeliness, performance, and diversity of the models you submit to Web-CAT.
Project Reports
Each problem culminates with a group project report. Two students will lead each team's report. Every student must lead at least one problem report.
Project Presentations
Each problem culminates with a group project presentation. Two students will lead each team's presentation. Every student must lead at least one problem presention.
Peer and self evaluations
At the end of each project, students will submit self and peer evaluations.
Homework
Students will complete a small number of homework assignments, either individual programming assignments or written assignments graded by in-class quizzes.
Final Exam
A written exam will be given during the final exam period.
Grading & Policies
Course Policies
University Policies
ASU has official policies that are considered part of this syllabus, including the Academic Integrity Code,
accommodations for students with disabilities, Attendance Policy, and the Statement on Student Engagement with
Courses:
Syllabi Policy and Statement Information
Additional Syllabus Statements
In addition, this syllabus incorporates the following statements on Food Insecurity, Title IX Reporting
Obligations, and Public Sharing of Course Materials:
Optional Syllabi Policy and Statement Information
Grading
- The final grade will determined using the following percentages:
-
Category Percent Group Work 20% Modeling 20% Presentations and Reports 15% Peer Evaluations 20% Homework 10% Final 15% Total 100% - All grades will be posted to ASULearn.
- The maximum percentage required to earn each letter grade is shown below:
-
GradePercentage Required
A 93 A- 90 B+ 87 B 83 B- 80 C+ 77 C 73 C- 70 D+ 67 D 63 D- 60 F below 60 NOTE: Students in 4542 may receive the full grade range. Those in CS 5750 with a grade below 70 will recieve an F.
Calendar
The official sequence of class activities is maintained on ASULearn.