Oct 5, 2015: Kaggle Part IPart I of the Kaggle competition has begun! Please see the Kaggle menu link for more details.
Sep 23, 2015: HW 1 CorrectionThere was a small typo in HW 1 (which has now been corrected). Part b of problem 5 should read: "The bias component of this expected test MSE?".
Sep 23, 2015: Kaggle Data Terms and ConditionsA word from our ALS data partners: To participate in the course ALS prediction challenge, you will need to agree to these terms and conditions. If you cannot agree to those terms and conditions, please let us know by emailing the staff list.
Sep 23, 2015: LabsOccasionally we will post links to "labs" which supplement the day's lecture. These labs feature code and output produced by the course staff to illustrate a concept. For instance, Lab 2 (under the Lectures tab) shows you how we generated the bias-variance decomposition example in today's lecture. Feel free to read through the lab to improve your understanding and to try your hand at recreating or modifying our examples.
Sep 21, 2015: Finding Kaggle Competition TeammatesWe've created a pinned post on Piazza to help you find Kaggle competition teammates.
Stats 202 meets MWF 1:30-2:20 pm in NVIDIA Auditorium.
All lectures will be recorded on video by the Stanford Center for Professional Development and posted here. If that link does not work for you, try logging into http://scpd.stanford.edu/ directly and navigating to the Stats 202 course. If you are unable to access the lecture videos, please contact SCPD to gain access.
Lecture slides will be posted on this site (see the Lectures link on the left).
Stats 202 is an introduction to Data Mining. By the end of the quarter, students will:
Introductory courses in statistics or probability (e.g., Stats 60), linear algebra (e.g., Math 51), and computer programming (e.g., CS 105).
The vast majority of questions about homework, the lectures, or the course should be asked on our Piazza forum, as others will benefit from the responses. You can join the Piazza forum using the link www.piazza.com/stanford/fall2015/stats202. We strongly encourage students to respond to one another's questions!
Questions from which others cannot benefit can be emailed to the staff mailing list email@example.com.
Personal staff email addresses should only be used for sensitive matters (e.g., concerns about specific course staff).
Consult this table for up-to-date office hour information. For online office hours, we provide persistent meeting links which will be active at the advertised office hour times. Upon clicking the link, you will have the option of joining the meeting by phone, browser, or BlueJeans app.
|Instructor||Lester Mackey||W 11-11:55am, 2:30-3:30pm||Sequoia 141|
|TA||Murat Erdogdu||Tu 9-10:55am||Sequoia 206|
|TA||Jackson Gorham||M 6-7pm, Tu 6-7pm||Online office hours: https://bluejeans.com/287714272|
|TA||Minyong Lee||M 10-12pm||Sequoia 237|
|TA||Paulo Orenstein||Th 4-6pm||Sequoia 105 (the library)|
|TA||Charles Zheng||Th 8-10pm (online), F 9-11am (Sequoia 207), F 6-8pm (online)||Online office hours: https://bluejeans.com/889567289|
The only textbook required is An Introduction to Statistical Learning with Applications in R by Gareth James, Daniela Witten, Trevor Hastie, and Robert Tibshirani (Springer, 1st ed., 2013).
The book is available at the Stanford Bookstore and free online through the Stanford Libraries.
We may occasionally assign (optional) supplementary readings from the optional text The Elements of Statistical Learning by Hastie, Tibshirani, and Friedman (Springer, 2nd ed.).
In our lecture notes, the abbreviation ISL = Introduction to Statistical Learning and ESL = Elements of Statistical Learning.
(If you are an online SCPD student, please see SCPD info for more information on remote exam instructions and timings.)
If for extenuating circumstances you cannot take the midterm on Oct. 26, you must email us by Oct. 14. Since the midterm is during class, we cannot guarantee an opportunity to make it up.
The final exam is mandatory. If you cannot take it at the time indicated above, please drop the class.
There will be 7 graded homework assignments, due on Wednesdays at the start of class. An ungraded assignment (Homework 0) will help you install and become familiar with the tools used in this course. The homework assignments and staff solutions will be posted on this website and will be accessible by enrolled students (see the Homework link on the left).
After attempting homework problems on an individual basis, you may discuss a homework assignment with up to two classmates. However, you must write up your own solutions individually and explicitly indicate with whom (if anyone) you discussed the homework problems at the top of your homework solutions. In your solutions, please show your work and include all relevant code written. Please also keep in mind the university honor code.
This quarter, we will be using the Gradescope online submission and scoring system for all homework submission. Gradescope will send a Stats 202 enrollment notification to your Stanford email address. If you have not received such a notification by Thursday Sep. 24 (Pacific Time), please add your enrollment information to this spreadsheet and allow 24 hours for us to process your enrollment.
Your problem sets should be submitted as PDF or image files through Gradescope. Here are some tips for scanning and submitting through Gradescope.
Any regrade requests should be submitted through Gradescope within one week of receiving your grade. Please, read the relevant solutions and review the relevant course material prior to sending a request and specify (1) the part(s) of the homework you believe were wrongly graded and (2) why you deserve additional credit. We reserve the right to regrade the entirety of any homework for which any regrade is requested.
Late homework will not be accepted, but the lowest homework score will be ignored.
An important part of the class will be an in-class prediction challenge hosted by Kaggle. This competition will allow you to apply the concepts learned in class and develop the computational skills to analyze data in a collaborative setting.
To learn more about the competition see the link on the left.
|Mon 9/21||Class logistics, HW 0||HW 0 out|
|Wed 9/23||Supervised and unsupervised learning||2||HW 1 out|
|Fri 9/25||Principal components analysis||10.1,10.2,10.4||HW 0 due|
|Mon 9/28||Clustering||10.3, 10.5|
|Wed 9/30||Linear regression||3.1-3.3||HW 1 due, HW 2 out|
|Fri 10/02||Linear regression||3.3-3.6|
|Mon 10/05||Classification, logistic regression||4.1-4.3|
|Wed 10/07||Linear discriminant analysis||4.4-4.5||HW 2 due, HW 3 out|
|Fri 10/09||Classification lab||4.6|
|Mon 10/12||Cross validation||5.1|
|Wed 10/14||The Bootstrap||5.2-5.3||HW 3 due, HW 4 out|
|Fri 10/16||Regularization||6.1, 6.5|
|Wed 10/21||Shrinkage lab||6.6||HW 4 due|
|Fri 10/23||Dimension reduction||6.3, 6.7|
|Mon 10/26||Midterm exam|
|Wed 10/28||Splines||7.1-7.4||HW 5 out|
|Fri 10/30||Smoothing splines, GAMs, Local regression||7.5-7.7|
|Mon 11/02||Non-linear regression lab||7.8|
|Wed 11/04||Decision trees||8.1, 8.3.1-2||HW 5 due, HW 6 out|
|Fri 11/06||Bagging, random forests, boosting||8.2, 8.3.3-4|
|Mon 11/09||Support vector machines||9.1-9.2|
|Wed 11/11||Support vector machines||9.3-9.5||HW 6 due, HW 7 out|
|Fri 11/13||Support vector machines lab||9.6|
|Mon 11/16||Non-linear dimensionality reduction|
|Wed 11/18||Wavelets||HW 7 due|
|Fri 11/20||Data scraping, data wrangling|
|Mon 11/30||Web visualizations|
|Wed 12/02||Final review||All chapters||Kaggle deadline|
|Fri 12/04||Final review||All chapters|
|Mon 12/07||Final exam|