CMSC454, Algorithms for Data Science, Fall 2023
Instructor: Aravind
Srinivasan
Class Venue and Time: CSI 3117, TuTh 9:30AM - 10:45AM
tl;dr -- Students: please add
yourselves to the Piazza page for
the class! We will use Piazza extensively.
Administrative Details
Instructor:
Aravind
Srinivasan
Office: IRB 4164, Phone: 301-405-2695
Instructor's Office Hours (Updated): Tue, Thu 11AM--noon at IRB 4164, or by
appointment (please email Aravind)
TA:
Nitya Raju (nraju AT umd), Ruibo Chen (rbchen At umd).
TA Office Hours and Location: Nitya Raju: Mon 1PM-2PM, Wed 3PM-4PM, AVW 4160; Ruibo Chen: Mon 10AM-11AM, Thu 4PM-5PM, AVW 4160.
Course Time and Location: Tue, Thu
9:30AM-10:45AM, CSI 3117
Course Webpage:
https://www.cs.umd.edu/class/fall2023/cmsc454.
Textbook, Lecture Notes and Related Resources
There is no required text.
We will have a good deal of overlap with (but will not be identical to)
Prof. Cameron Musco's class.
We will also use two free online books as references for part of the class:
Foundations of Data Science by Avrim Blum, John Hopcroft, and Ravi Kannan; and
Mining of Massive Datasets by Jure Leskovec, Anand Rajaraman, and Jeff Ullman.
Aravind's Pledge to the Students
Your education is very important to me, and I respect each of you regardless
of how you do in the class. My expectations of you are that you attend class
and pay full attention, and give enough time to the course.
I strongly encourage you to ask questions in class,
and to come to the office hours (mine or the TA's)
with any further questions. We can have a
very enjoyable educational experience if you pay attention
in class, give sufficient time to our course,
and bring any difficulties you have promptly to our attention. I look forward
to our interaction both inside and outside the classroom.
Course Overview
This course presents some fundamental algorithmic approaches in data science.
The approach will be mathematical/algorithmic and proof-based, but attention will be drawn regularly to real-world data-science concerns that motivate
our problems and approaches.
Topics include: probability review; data verification on the cloud; hashing,
concentration bounds, and Bloom filters; sketching and streaming; Nelson-Yu
approximate counting; high-dimensional geometry; dimension reduction and the
Johnson-Lindenstrauss Lemma; linear-algebra review; PCA; low-rank
approximation; SVD; spectral clustering; gradient descent and its relatives;
differential privacy; fairness in data science, and related topics.
Grading, Homework, and Exams
There will be a combination of written homeworks (about six), a midterm exam, and a comprehensive final exam.
All homework is to be done individually, but discussions with
classmates are encouraged: just list the classmates you discussed the assignment with.
Weight of assignments: homework (45% total), midterm exam (20%),
and final exam (35%).
The mid-term date: TBA;
the final exam 8-10AM on Thursday, Dec. 14. Both will be in class.
Unless otherwise noted, the following late policy shall be applied to all homework:
- Up to 6 hours late: 5% of total
- Up to 24 hours late: 10% of the total
- For each additional 24 hours late: 20% of the total
The lowest homework score will be dropped.
Course Evaluation
Students are strongly encouraged to complete their course evaluations;
please do so at the CourseEvalUM
website when it is ready.
Academic Accommodations for Disabilities
Any student eligible for and requesting reasonable academic accommodations
due to a disability is requested to provide, to the instructor in class or by email, a letter of accommodation from the Office of Disability Support
Services (DSS) within the first two weeks of the semester.
Excused Absences, Academic Integrity, and Additional Info.
The policy on excused absences is at https://www.ugst.umd.edu/V-1.00(G).html.
Please see the university's policies on various important issues.
Mandatory Reporting
Notice of mandatory reporting of sexual assault, sexual harassment, interpersonal violence, and stalking: As a faculty member, Aravind Srinivasan is
designated as a “Responsible University Employee,” and must report all disclosures of sexual assault,
sexual harassment, interpersonal violence, and stalking to UMD’s Title IX Coordinator per University
Policy on Sexual Harassment and Other Sexual Misconduct.
If you wish to speak with someone confidentially, please contact one of UMD’s confidential resources,
such as CARE to Stop Violence
(located on the Ground Floor of the Health Center) at 301-741-3442 or the
Counseling Center (located at the Shoemaker Building)
at 301-314-7651.
You may also seek assistance or supportive measures from UMD’s Title IX Coordinator,
Angela Nastase, by calling 301-405-1142, or emailing titleIXcoordinator@umd.edu.
To view further information on the above, please visit the
Office of Civil Rights and Sexual Misconduct's website.
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