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TuTh 9:55 - 11:10, HORN HALL 103C
Computational Linear Algebra
MoTuWe 2:00 - 4:00
SuMoWe 7:30 - 9:00 p.m.
Linear algebra is a key tool for solving problems in diverse fields. Solving these problems on a large scale requires computers, which poses new challenges.
This course focuses on developing tools from linear algebra, programming intensively, and solving application problems.
Prerequisite Linear Algebra (Math 250)
Textbook Numerical Analysis (3rd ed.) by Sauer
Online Component glow.williams.edu
Software RStudio and R
Algorithm Choice and Evaluation
Practice and expand skills
Check basic understanding
Get personal attention from professor
Discuss and ask questions about material
Focus on calculations and concepts
Collaborate when allowed
Seek help when needed
Pre-class checkpoints (10%), 3 dropped
Open book, no collaboration
Due by start of class, can take twice for average grade
Homework (30%) 1 dropped Collaboration allowed
Final exam (20%)
24-hour self-scheduled exam
No collaboration, no corrections
We focus on learning and mastery, reserving discussions of grades only for cases in which I have made a clerical error.
In case of technical problems, illness, stress, and other issues, the drop policy applies. Missed/late work will receive a grade of zero, but your lowest three checkpoint scores and lowest homework score will be automatically dropped.
For individual work for which collaboration is indicated as being allowed, you can exchange ideas and approaches, but it is expected that in the end, you build the intellectual scaffolding of the work you submit. One way to make sure you respect this policy is to refrain from joint step-by-step problem solving, and to wait to write up problems until you are on your own and are working independently. If you collaborate, always cite your collaborator(s).
Participation in class is required because it supports our learning community. If your participation falls below acceptable standards, I reserve the right to reduce your final course grade. Participation depends on you being both present and engaged. I don't distinguish between excused and inexcused absences, so please do not notify me if you need to miss class.
If you would like me to write you a letter of recommendation, I require at least four weeks' advance notice.
R programming modules in DataCamp (5%)
2 midterms (25%) No collaboration Corrections optional (collaboration allowed)
In-class activities (10%)
Open book, done in assigned groups
Graded for effort; aim to complete over 1/2 of each for full credit
You deserve to be addressed in the manner you prefer. To guarantee that I address you properly, you are welcome to tell me your pronoun(s) and/or preferred name at any time, either in person or via email.
We embrace diversity of age, background, beliefs, ethnicity, gender, gender identity, gender expression, national origin, religious affiliation, sexual orientation, and other visible and non-visible categories. I do not tolerate discrimination.
I want you to succeed in this course. Contact [email protected] for learning accommodations. For personal issues, stress, health problems or life circumstances, contact the Dean's office at x4171. Contact me if you have other special circumstances. I will find resources for you.
You deserve a community free from discrimination, sexual harassment, a hostile environment, sexual assault, domestic violence, dating violence, and stalking. If you experience or know of a Title IX violation, you have many options for support and/or reporting; see titleix.williams.edu.
The honor code is a cornerstone of our learning community and of this course. This syllabus (above) specifies when collaboration is allowed. It is your responsibility to know and follow academic integrity policies. I will gladly answer any questions you have.