STATS205: Introduction to Nonparametric Statistics
Stanford / Autumn 2019-2020
- 9/23: Welcome to STATS205!
See Google Calendar
for office hours. Please always check the Google calendar before going to an office hour
, because the office hours may change. (Although often we won't change within one or two days.)
Please use Piazza
for questions and discussions.
nonparametric regression and nonparametric density estimation, nearest neighbor algorithms, modern nonparametric techniques, nonparametric confidence interval estimates, estimating CDFs, wavelet, connection to overparameterized neural networks.
- basic courses in statistics (e.g., knowledge of regression)
- linear algebra
- either R or python programming
- Homeworks (50%): there will be four homeworks. The assignments can be done individually or in pairs. If you work in a pair, only one member should submit all of the relevant files, and in the homework you should note the two contributors' names and sunet IDs.
Each homework must be submitted through Gradescope. Sign up for the course using entry code T9VED24.
You are encouraged to use LaTeX to writeup your homeworks
(here's a template), but this is not a requirement. You will receive
one (1) bonus point for submitting a typed written assignment (e.g. LaTeX, Microsoft Word).
We will accept scanned handwritten assignments but they will not receive the bonus point.
You are not required to work with the same people on each problem set --- you're welcome to work in a pair on one problem set, individually on the next, in a pair with a different partner the next time, etc. If you do work in a pair, please
note that both members of the pair are responsible for ensuring that each assignment is completed and
submitted on time.
If you submit in a pair, you should submit just a single set of solutions. Both members of the pair will
earn the same grade on the problem set.
- Project (40%):
The project can be done in pairs. The page limit for project report is 8 pages, not including reference or appendix. See this Google doc for more instructions.
- Scribe notes (10%): You will be asked to scribe a note for a lecture (or two halves of the lectures) in LaTeX. The course staff will select one note for each lecture and share it with other students. 2% bonus credit will be given if your note is selected for posting. See this Google doc for the detailed guidelines. The scribe notes are due 4 days after the lecture (11pm Tues). Please sign up here before Sept 30th and plan the time ahead.
- Piazza: You will be awarded with up to 2% extra credit if you answer other students' questions in a substantial and helpful way.
Late policy: No late days are allowed for scribe notes. Each student will have a total of three free late (calendar) days to use for your submissions including the homeworks and projects, excluding scribe notes. Each 24 hours or part thereof that a homework is late uses up one full late day. Once these late days are exhausted, any assignments turned in late will be penalized 33.4% per late day. Late work done in pairs with x late days will require x late days from each of the contributors. Under extentuating circumstances, you may request an extension by contacting the course staff. Please note that, to ensure fairness, these circumstances do not include events that are somewhat predictable, such as job interviews or conference deadlines.
Please follow the honor code
(subject to change)
Week 1 Week 2
- Mon 09/30: Homework 1 out
- Fri 10/04: Lecture 2
- Wed 10/09: Homework 1 due
- Thu 10/10: Homework 2 out
- Fri 10/11: Lecture 3
- Fri 10/18: Lecture 4: Guest lecture
- Wed 10/23: Homework 2 due
- Thu 10/24: Homework 3 out
- Fri 10/25: Lecture 5:
- Wed 10/30: Project proposal due (extend to Nov 1st, Friday)
- Fri 11/01: Lecture 6:
Week 8 Week 9
- Wed 11/06: Homework 3 due
- Thu 11/07: Homework 4 out
- Fri 11/08: Lecture 7
Week 10 Final's week
- Wed 11/20: Homework 4 due
- Fri 11/22: Lecture 9
- Thur 12/12: Final project due