Full Syllabus: Syllabus_AMATH301_Gin.pdf
All three sections (A, B, and C) will be exactly the same. Nothing about the course will be specific to an individual section. You should therefore feel free to sign up for (or switch into) whichever section allows you to be in the other courses you want to take. The limits on the number of students in each section have been removed.
All lectures will be pre-recorded using Panopto and can be viewed at any time. You will not have to attend class live.
There will be weekly homework assignments due on Fridays. The assignments will be posted to Canvas one week before they are due and turned in via MATLAB Grader and/or Gradescope.
This class includes activities that were designed to be completed in class in small groups. Students will still complete the activities, but they will now be done online. You are encouraged to form groups of 2-3 students and complete them together, but you are allowed to work alone. They can be completed at any time during the week they are posted.
This course will have a midterm and final exam. Exams will be completed online, likely through Canvas. They will be timed and open note. You will be given a 24 hour time window in which they must be completed.
There will be designated times each week in which you can ask the TAs and/or the instructor questions about the programming assignments or other aspects of the course. These will be held via Zoom. The lab hours will begin during the second week of class (when the first assignment is due). The times for the lab hours will be posted during the first week of class.
MATLAB will be used heavily in this course so you need access to it. MATLAB licenses for students can be obtained for free from UWare. If you do not have a computer that can run MATLAB, you can rent a computer from the Student Technology Loan Program.
There is no required textbook for this class, but the course material comes from the textbook listed below. It is a useful resource and should be available through the University bookstore.
Data-Driven Modeling & Scientific Computation: Methods for Complex Systems & Big Data by J. Nathan Kutz.