Handout #1: Course Information
Meeting Times and Locations
Lectures: MW 1:30PM-2:50PM.
(Jordan Hall basement)
Discussion sections: Fridays, 1:30-2:50pm
Location: 420-040 (optional attendance)
Professor: John Duchi
Office: Sequoia 126
Course Coordinator: Swati Dube|
Office: Gates 127
Albert Haque (Head TA)
If you and have a homework,
technical or general administrative question about CS229, for you to
get the fastest possible response, please post it on
forum. To contact the CS229 teaching staff directly, you can also
For telephone numbers and information about office hours (where we can
help you in person), see Office Hours and
This course provides a broad introduction to
machine learning and statistical pattern recognition. Topics include:
supervised learning (generative/discriminative learning,
parametric/non-parametric learning, neural networks, support vector
machines); unsupervised learning (clustering, dimensionality reduction,
kernel methods); learning theory (bias/variance tradeoffs; VC theory;
large margins); reinforcement learning and adaptive control.
The course will also discuss recent applications of machine learning,
such as to robotic control, data mining, autonomous navigation,
bioinformatics, speech recognition, and text and web data
Students are expected to have the following background:
- Knowledge of basic
computer science principles and skills,
at a level sufficient to write a reasonably non-trivial computer program.
with the basic probability theory. (CS109 or Stat116 is sufficient but not
- Familiarity with the basic linear
algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than
There is no required text for this course.
Notes will be posted periodically on the course web site. The following
books are recommended as optional reading:
Richard Duda, Peter Hart and David Stork, Pattern Classification, 2nd ed. John Wiley & Sons, 2001.
Tom Mitchell, Machine Learning. McGraw-Hill, 1997.
Richard Sutton and Andrew Barto, Reinforcement Learning: An introduction.
MIT Press, 1998
Trevor Hastie, Robert Tibshirani and Jerome Friedman, The Elements of Statistical Learning. Springer, 2009
Course handouts and other materials can
be downloaded from http://www.stanford.edu/class/cs229/materials.html
Homeworks and Grading
There will be four written homeworks, one midterm,
and either a major open-ended term project or a 24
hour take-home final exam (the term project option will require
instructor approval). The homeworks will contain written questions and
questions that require some Matlab programming. In the term project,
you will investigate some interesting aspect of machine learning or
apply machine learning to a problem that interests you.
We try very hard to make questions unambiguous, but some ambiguities
may remain. Ask if confused or state your assumptions explicitly. Reasonable
assumptions will be accepted in case of ambiguous questions.
We strongly encourage students to form
study groups. Students may discuss and work on homework problems in groups.
However, each student must write down the solutions independently, and without
referring to written notes from the joint session. In other words, each
student must understand the solution well enough in order to reconstruct
it by him/herself. In addition, each student should write on the problem
set the set of people with whom s/he collaborated.
Further, since we occasionally reuse problem set questions from
previous years, we expect students not to copy, refer to, or look
at the solutions in preparing their answers. It is an honor code
violation to intentionally refer to a previous year's solutions.
This applies both to the official solutions and to solutions that
you or someone else may have written up in a previous year.
Each student will have a total of seven
free late (calendar) days to use for
homeworks, project proposals and project milestones.
Once these late days are exhausted, any assignments turned in late will be
penalized 20% per late day. However, no assignment will be accepted
more than three days after its due date, and late days cannot be used
for the final project poster or writeup. Each 24 hours or part thereof that a
homework is late uses up one full late day.
We will be using Gradescope to handle assignment submissions.
Hard copy and email submissions will not be accepted.
The term project may be done in teams of up to three persons. The
midterm is open-book/open-notes, and will cover the material of the
first part of the course. It will take place on Monday, May 9, 2016 from 6-9 pm (location TBD).
Assignment 0 is a dummy assignment that will allow you to get used to the Gradescope submission process, and it will be due one week from the release date. It is worth 0 points.
When you submit your assignment, make sure to tag all the pages for each problem according to Gradescope's submission directions. Graders may deduct points on problems that are difficult to find.
The due dates on Gradescope will be the hard deadlines, after which we will not accept submissions. On-time submissions should be made before the deadlines listed on the website. Do not confuse the hard deadline on Gradescope with the deadlines on the website.
We strongly encourage typesetting your assignments. If the grader cannot read your assignment for whatever reason (handwriting, photo/scanning quality, etc.), you will not receive credit for that work.
Regrades will also be handled through Gradescope. We will begin to accept regrades for an assignment the day after grades are released for a window of three days. We will not accept regrades for an assignment outside of that window. Regrades are intended to remedy grading errors, so regrade requests must discuss why you believe your answer is correct in light of the deduction you received. We do not accept regrade requests of the form "I deserve more points for this" or "that deduction is too harsh." If you submit a regrade request of this form, you will receive further deductions. When you submit a regrade request, the grader may review your entire assignment, in which case you may lose points on other questions. Your score on an assignment may decrease if you submit for a regrade.
To review material from the prerequisites or to supplement the lecture
material, there will occasionally be extra discussion sections held on
Friday. An announcement will be made whenever one of these sections is
held. Attendance at these sections is optional.
Communication with the Teaching Staff
If you have a question that is not confidential or personal, encourage
you to post it on our forum on Piazza.
To contact the teaching staff directly, we strongly encourage you to come to office hours.
If that is not possible, you can also email us at the course staff list,
of the TAs and the professor). By having questions sent to all of us,
you will get answers much more quickly. Of course, confidential or personal questions
can still be sent directly to Professor Duchi or the TAs.
For grading questions, please talk to us after class or during office
Regrade policies will be updated soon. Stay tuned.
Answers to commonly asked questions and clarifications
to the homeworks will be posted on the FAQ.