Stats 202 meets MWF 1:30-2:20 pm in Skilling Auditorium.
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:
Introductory courses in statistics or probability (e.g., Stats 60), linear algebra (e.g., Math 51), and computer programming (e.g., CS 105).
Consult this table for up-to-date office hour information.
|Instructor||Lester Mackey||Wednesday 2:30-4:30 pm||Sequoia 141|
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 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.
SCPD students will have to complete each exam in the amount of time specified and return it to SCPD within 24 hours of the time of the exam at Stanford.
There will be 7 graded homework assignments, due at the start of class on the day indicated.
This quarter, we will be trying an online submission and scoring system called Scoryst, which was developed by Stanford students. Homeworks will be submitted as PDF files on this website. Enroll in our site using the link on the header of each homework.
All regrade requests should be submitted to email@example.com. Please, read the solutions to the homework before sending a request and specify (1) the part(s) of the homework you believe were wrongly graded and (2) why you deserve full or partial credit.
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.
To learn more about the competition see the link on the left.
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 9/21||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|
Some important dates: