Dec 11, 2013:
The final exam grades are available on Coursework. The average was 79 with a standard deviation of 13. You can pick up your graded papers from my office on Friday from noon until 2pm, or by appointment next week. SCPD students will be sent their exams back tomorrow.
Dec 11, 2013:
The solutions to the final exam are now available here.
Dec 7, 2013:
Grade statistics are now available here.
Dec 3, 2013:
The Kaggle deadline has changed to Friday, December 6 on the website. However, the winners of the competition will still be determined on Wednesday December 4 at 4pm PST. Please, make all your submissions by tomorrow.
Nov 4, 2013:
Oct 31, 2013:
You may download the solutions to the midterm.
Oct 13, 2013:
Please send all regrade requests to the graders at email@example.com. Include:
Oct 7, 2013:
Both exams will be closed-book and closed-notes. We will provide a cheat sheet with the equations necessary. You will not need to recall R commands, but we may ask you to interpret the output of R functions without documentation.
Sep 30, 2013:
If you have questions about homework or any of the lectures, please use our Piazza forum. You may join it using the link: www.piazza.com/stanford/fall2013/stats202.
Any other questions can he emailed to the staff mailing list:
Stats 202 meets MWF 1:15-2:05 pm at Skilling Auditorium (note the location change!).
All lectures will be recorded on video by the Stanford Center for Professional Development and posted here.
Stats 202 is an introduction to Data Mining. By the end of the quarter, students will:
Consult this table for up-to-date office hour information.
|Instructor||Sergio Bacallado||Friday 2:15-3:45pm||Sequoia 202|
|TA||Rakesh Achanta (Unix, R, Python)||Tuesday 3:45-5:45pm||Sequoia 105|
|TA||Jackson Gorham (Unix, Git, Python)||Monday 3:45-5:45pm||Sequoia 207|
|TA||Jiyao Kou (R, Windows)||Wednesday 3:45-5:45pm||Sequoia "Fishbowl"|
|TA||Minyong Lee (R)||Tuesday 10am-12pm||Sequoia 207|
|TA||Jian Li||Friday 10am-12pm||Sequoia "Fishbowl"|
|TA||Scott Powers (R, Mac, Windows)||Thursday 2:10-4:10pm||Bldg 320 Room 107|
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. A hard copy of the book is in the reserves of the Mathematics and Statistics Library.
If for extenuating circumstances you cannot take the midterm on October 28, you must email us by October 15. 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 on the time indicated above, please drop the class.
There will be 7 graded homework assignments, due at the start of class on the day indicated. You may:
Late homework will not be accepted, but the lowest homework score will be ignored.
An important part of the class will be a quarter-long 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.
Your goal will be to predict the employment status of middle-aged individuals during the 2008 financial crisis. We will use data from an ambitious longitudinal study made available by the National Bureau of Labor Statistics.
To learn more about the competition see the link on the left.
Invitations to the competition have been sent! If you haven't received one, please contact us. You may use our Piazza forum to form teams.
The 3 teams who obtain the highest score in the Kaggle competition will be given the option of not taking the final exam (!). Their class grade would be based on midterm and homework scores alone.
|Mon 9/23||Class logistics, HW 0||HW 0 out|
|Wed 9/25||Supervised and unsupervised learning||2||HW 1 out|
|Fri 9/27||Principal components analysis||10.1,10.2,10.4||HW 0 due|
|Mon 9/30||Clustering||10.3, 10.5|
|Wed 10/02||Linear regression||3.1-3.3||HW 1 due, HW 2 out|
|Fri 10/04||Linear regression||3.3-3.6|
|Mon 10/07||Classification, logistic regression||4.1-4.3|
|Wed 10/09||Linear discriminant analysis||4.4-4.5||HW 2 due, HW 3 out|
|Fri 10/11||Classification lab||4.6|
|Mon 10/14||Cross validation||5.1|
|Wed 10/16||The Bootstrap||5.2-5.3||HW 3 due, HW 4 out|
|Fri 10/18||Regularization||6.1, 6.5|
|Wed 10/23||Shrinkage lab||6.6||HW 4 due|
|Fri 10/25||Dimension reduction||6.3, 6.7|
|Mon 10/28||Midterm exam|
|Wed 10/30||Splines||7.1-7.4||HW 5 out|
|Fri 11/01||Smoothing splines, GAMs, Local regression||7.5-7.7|
|Mon 11/04||Non-linear regression lab||7.8|
|Wed 11/06||Decision trees||8.1, 8.3.1-2||HW 5 due, HW 6 out|
|Fri 11/08||Bagging, random forests, boosting||8.2, 8.3.3-4|
|Mon 11/11||Support vector machines||9.1-9.2|
|Wed 11/13||Support vector machines||9.3-9.5||HW 6 due, HW 7 out|
|Fri 11/15||Support vector machines lab||9.6|
|Mon 11/18||Prediction with time series|
|Wed 11/20||Prediction with relational data||HW 7 due|
|Fri 11/22||Data scraping, data wrangling|
|Mon 12/02||Web visualizations|
|Wed 12/04||Final review||All chapters||Kaggle deadline|
|Fri 12/06||Final review||All chapters|
|Mon 12/09||Final exam|
Some important dates: