Math 36600: Course Information
Course Title: Introduction to Applied Mathematical Computation
Prerequisites: Math 34600 and CSC 10200 or 10300.
Catalog Description: Calculus, linear algebra, elements of
probability theory and combinatorics are examined through use of
Matlab. Topics selected from symbolic and numerical problems in
analysis; matrices, linear mappings, eigenvalues and applications;
queueing theory; random numbers and simulations; graphics.
Semester: Spring 2020.
Meeting time and place: TuTh 2:00-3:15pm on Blackboard Collaborate Ultra.
- Name: Prof. Pat Hooper
- Office Hours: Tuesdays 3:50-4:50pm via Zoom.
- Office: NAC 6/282
- Email: email@example.com
All texts above are available as a free download from the City College library. Please follow the links above. (If you are off campus, you will need to sign in to the library with your barcode to download the books.)
Notice of Deviation from Catalog Description: While the Catalog Description of the course says that we will use MATLAB, this section of the course will primarily use Python instead. Please contact me in the first two days of class if you feel strongly about using MATLAB instead of Python, and I will try my best to accomodate you. By remaining in this section of the course and not discussing this with Prof. Hooper, you agree to use Python rather than MATLAB.
Python is a more broadly applicable programming language than MATLAB, and it us used by famous and important companies. It is widely used in Data Science. It is also free. You can install MATLAB for free on your computer because CUNY reached an agreement with the publisher of MATLAB, but it will no longer be free when you graduate. In additon, Python is open-source and has a large active community of users. In general, I believe it is much more valuable progamming language to learn than MATLAB. The following website gives a more comprehensive analysis along these lines:
General expectations: This is a challenging course. This course requires prior understanding of Linear algebra, Calculus, and Differential equations. In addition to these topics, we will expect you to be able to learn some basic combinatorics. You should also have some experience with computer programming. We will be using Python, but prior experience with Python is not necessary.
Proofs are a fundamental part of both mathematics and programming. You are expected to be able to write basic proofs of mathematical statements, and proofs related to algorithms. The claim that an algorithm terminates or returns the correct solution requires proof.
For each hour spent in the classroom, I expect you to spend at least three hours reading and understanding the book, understanding lecture notes, doing homework, and programming. Practice (doing problems, proofs, and programming) is an important part of learning. Only adequate practice will guarantee that you can complete midterm and final exam problems in a timely manner.
The best way to learn something well is to find something that interests you and do it. As we move through the course, try to find applications of the ideas to things you are interested in.
Expectations of written work: Mathematical computations, proofs, and programs will be graded
partially on presentation. In order to receive full credit, a student who reads your answer
should be able to easily understand how you solved the problem. Written work is expected
to be legible and arguments are expected to be well articulated.
Grades will be computed from the following:
- Homework, Classwork, and Programming Assignments (40%)
- Midterms (30%)
- Final Exam (30%)
Your final score will be tabulated out of 100% as indicated by the percentages above. A letter grade will be assigned to you according to the following list: A+ (97-100), A (95-96), A- (90-94), B+ (87-89), B (84-86), B- (80-83), C+ (77-79), C (74-76), C- (70-73), D (60-69), F (0-59).
The midterms will be held on Tue, Feb 25 and Thu, Apr 2.
You will be given the full class to complete each midterm. If one midterm is missed under well documented and sufficiently compelling circumstances, then it will not count towards your midterm grade. This policy is only applicable for the first midterm missed. All subsequent midterms missed under under well documented and sufficiently compelling circumstances will result in a the offer of a makeup midterm being given. The makeup must be taken within one week of the original midterm (again unless there are additional well documented and sufficiently compelling circumstances), and the failure to take a makeup within this time period will result in a zero assigned for the midterm grade. Notify me ahead of a midterm you expect to miss to be sure your circumstances are sufficiently compelling.
Homework assignments: Homework will be assigned approximately once a week and will have a due date.
Homework assignments will be made available on the course website at least one week before the assignment is due.
I encourage you to work in groups on the homework problems, especially if this best suits your learning style. Nonetheless, you should be confident that you understand how to do each problem, and should be able to solve similar problems independently. Failure to ensure that you can solve problems independently will surely have a negative effect on exam grades.
Late homework: Late homework will not be accepted for any reason. If you need to miss class, please scan it and email your assignment to me as a PDF document before the start of class on the day it is due.
Dropped homework grades: The two lowest homework grades will be dropped.
Course information, homework assignments, and documents can be found
on the website:
I will use blackboard to collect some homework assignments and to keep track of your grades. You can view your grades
there. To access blackboard visit:
You are expected to adhere to the CUNY Policy on Academic Integrity. This policy is posted at
In particular, it is expected that you not plagarize. This has been especially problematic on homework. Your homework must not copy from another source, and you must cite any sources used when preparing your solutions. Sources can include textbooks, webpages, discussions with other people, and other student work. Citations should be as specific as possible. All submitted work must be written in your own words.