Linear algebra, multivariate calculus, and their applications in real world problems such as optimization, clustering, image compression, compressed sensing, matrix completion, and neural network.
Please follow the UCLA COVID protocols.
Tuesdays and Thursdays, 10am-11:50am @ CHS 61-235.
Dr. Hua Zhou
Office: CHS 21-254A
Email: huazhou@ucla.edu
Office hours: Tuesdays 12am-1pm and Thursdays 2pm-3pm @ Zoom https://ucla.zoom.us/j/98224611941.
Tomoki Okuno: tomokiokuno0528@ucla.edu
Office hours: Wednesday 5pm-6pm @ CHS 41-235 and Friday 2pm-3pm @ Zoom https://ucla.zoom.us/j/98224611941.
This 4-unit course is designed for first year biostatistics MS and PhD students. It will review, and in some cases introduce, specialized topics in Linear Algebra, Multivariate Calculus and Scientific Computing that are considered to be particularly pertinent for the subsequent courses in our MS and PhD curriculum. It is required for biostatistics MS students and PhD students.
See the schedule page for a tentative list of topics.
Prerequisites are the same as those for admission into biostatistics MS program. Students should have seen one course in basic linear algebra at the level of UCLA’s Math 33A (using a textbook such as O. Bretscher, Linear Algebra, 5th Ed., Prentice Hall.) and a Calculus sequence at the level of UCLA’s Math 31AB (e.g. J. Rogawski, Calculus, 3rd Edition , W.H. Freeman & Co). Biostatistics MPH students will need to obtain consent from the instructor to take this course.
Students with more advanced mathematical preparation prior to admission may not need to take this course and should consult with Professor Hua Zhou and their academic advisers. For example, if you are comfortable answering the questions in 2021 final and 2019 take-home final, then this course is too easy for you.
https://ucla-biostat-216.github.io/2023fall
Course announcements and homework assignments will be sent from Bruin Learn system:
https://bruinlearn.ucla.edu/courses/168194
Recommended books (not required):
Boyd and Vandenberghe, Introduction to Applied Linear Algebra: Vectors, Matrices, and Least Squares, Cambridge University Press (2018). Authors kindly provide a free copy online.
The first couple of weeks heavily use material in this textbook, which is referred as BV in lecture notes. Professor Vandenberghe teaches EE133A out of this book this quarter.
Strang, Linear Algebra and Learning From Data, Wellesley, MA: Wellesley-Cambridge Press (2019), UCLA library link. Professor Strang taught a course out of this book at MIT.
Banerjee and Roy, Linear Algebra And Matrix Analysis For Statistics, 2nd Edition, CRC Press (2014), UCLA library link
Magnus and Neudecker, Matrix Differential Calculus With Applications in Statistics and Econometrics, John Wiley & Sons, Inc. (2019), UCLA library link
Class attendance is mandatory. If you are not able to attend a lecture due to medical or technical reasons, please proactively communicate with the instructor and TA and notify us your circumstances.
7 to 8 homework assignments.
In-class midterm on Oct 26. In-class final exam Dec 11 8am-11am (location TBD).
Attendance (20%) + Homework (20%) + midterm (20%) + final exam (40%).
Students needing academic accommodation based on a disability should contact the Center for Accessible Education (CAE) at (310)825-1501 or in person at Murphy Hall A255. When possible, students should contact the CAE within the first two weeks of the term as reasonable notice is needed to coordinate accommodations. For more information visit https://www.cae.ucla.edu.
ADA Contact:
Nickey Woods
Center for Accessible Education
A255 Murphy Hall.
Phone: (310)825-1501
TTY/TTD: (310)206-6083
Fax: (310)825-9656
UCLA’s Office for Equity, Diversity, and Inclusion provides resources, events, and information about current initiatives at UCLA to support equality for all members of the UCLA community. I hope that you will communicate with me or your TA if you experience anything in this course that does not support an inclusive environment, and you can also report any incidents you may witness or experience on campus to the Office of Equity, Diversity, and Inclusion on their website https://equity.ucla.edu.
Message about Academic Integrity to All UCLA Students from UCLA Dean of Students: UCLA is a community of scholars. In this community, all members including faculty staff and students alike are responsible for maintaining standards of academic honesty. As a student and member of the University community, you are here to get an education and are, therefore, expected to demonstrate integrity in your academic endeavors. You are evaluated on your own merits. Cheating, plagiarism, collaborative work, multiple submissions without the permission of the professor, or other kinds of academic dishonesty are considered unacceptable behavior and will result in formal disciplinary proceedings usually resulting in suspension or dismissal.
Forms of Academic Dishonesty: As specified in the UCLA Student Conduct Code, violations or attempted violations of academic dishonesty include, but are not limited to, cheating, fabrication, plagiarism, multiple submissions or facilitating academic integrity:
• Allowing another person to take a quiz, exam, or similar evalution for you
• Using unauthorized material, information, or study aids in any academic exercise or examination – textbook, notes, formula list, calculators, etc.
• Unauthorized collaboration in providing or requesting assistance, such as sharing information
• Unauthorized use of someone else’s data in completing a computer exercise
• Altering a graded exam or assignment and requesting that I be regraded
Plagiarism: Presenting another’s words or ideas as if they were one’s own
• Submitting as your own through purchase or otherwise, part of or an entire work produced verbatim by someone else
• Paraphrasing ideas, data or writing without properly acknowledging the source
• Unauthorized transfer and use of someone else’s computer file as your own
• Unauthorized use of someone else’s data in completing a computer exercise
Multiple Submissions: Submitting the same work (with exact or similar content) in more than one class without permission from the instructor to do so. This includes courses you are currently taking, as well as courses you might take in another quarter.
Facilitating Academic Dishonesty: Participating in any action that compromises the integrity of the academic standards of the University; assisting another to commit an act of academic dishonesty
• Taking a quiz, exam, or similar evaluation in place of another person
• Allowing another student to copy from you
• Providing material or other information to another student with knowledge that such assistance could be used in any of the violations stated above (e.g., giving test information to students in other discussion sections of the same course)
• Altering data to support research
• Presenting results from research that was not performed
• Crediting source material that was not used for research
While you are here at UCLA, if you are unsure whether what you are considering doing is cheating, don’t take chances – ask your professor. In addition, avoid placing yourself in situations which might lead your professor to suspect you of cheating.
Alternatives to Academic Dishonesty
• Seek out help – Meet with your professor, ask for assistance as needed.
• Ask for an extension – if you explain your situation to your professor, she/he might be able to grant you an extended deadline for an upcoming assignment.
• See a counselor at Student Psychological Services, and/or your school, college or department – UCLA has many resources for students who are feeling the stresses of academic and personal pressures.
If you would like more information, please come see us at the Dean of Students’ Office in 1206 Murphy Hall, call us at (310)825-3871 or visit their website at https://www.deanofstudents.ucla.edu.