Handout #1: Course Information
Meeting Times and Locations
Lectures: MW 9:00AM-10:15AM.
NVIDIA Auditorium (in the Huang Engineering Center)
Discussion sections: Fridays, 2:15-3:05
Location: Gates B01 (optional attendance)
Professor: Andrew Ng
Office: Gates 156
Head TA: Andrew Maas|
Office: Gates 114
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 our Piazza forum.
To contact the CS229 teaching staff directly, you can also email us
For telephone numbers and information about office hours (where we can help you
in person), see Office Hours and Contact
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:
Christopher Bishop, Pattern Recognition and Machine Learning. Springer, 2006.
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
- Home page: http://cs229.stanford.edu/
- Current quarter's class videos: Available from SCPD
- Piazza forum: https://piazza.com/class#fall2012/cs229
mailing list: firstname.lastname@example.org
(to contact the teaching staff directly)
NOTE: If sending email about a homework, please
state in the subject line which assignment and which question the email
refers to (e.g., Subject: Hwk3 Q1). Please send one
question per email. If you have a technical or homework or general administrative
question that is not confidential or personal, we encourage you to post
it on the Piazza forum instead,
as that will get you a faster response.
Homeworks and Grading
There will be four written homeworks,
one midterm, and one major open-ended term project. 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.
After you get back a graded homework, you may also be able to regain up
to 1/4 of lost points on the homework by submitting a corrected version of
a solution. (Because of scheduling and registrar
constraints, this will apply only to homeworks 1-3.) The corrected solution to homework N should be stapled on top of
homework N and submitted together with homework N+1. Write clearly which questions
you are correcting. Do NOT staple the homeworks N and N+1 together or else it will
not be graded. More details about
this will be provided in later handouts.
Honor code: 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.
Late assignments: 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 four days after its due date, and late days cannot be used
for the final project writeup. Each 24 hours or part thereof that a
homework is late uses up one full late day.
Assignment submission: To hand in an assignment,
write down the date and time of submission, and leave it in the submission
box near/outside Gates 188 and 182. Please don't disturb the staff in
those offices; directions to the hand-in box are
It is an honor code violation to write down the wrong time.
Regular (non-SCPD) students should submit hardcopies of all four written homeworks.
Please do not email your homework solutions to us.
If you are an SCPD student, you should email your solutions to us
email@example.com . Write
"ATTN: CS229 (Machine Learning), Problem Set PID" on the Subject of the email,
where PID is the problem set number (1/2/3/4).
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 November 7, 6-9pm (location TBD).
Course grades: will be based 40% on homeworks (10% each),
20% on the midterm, and 40% on the major term project. Up to 2% extra
credit may be awarded for class participation, such as for helping
classmates on the
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 Ng or the TAs.
For grading questions, please talk to us after class or during office
hours. If you want a regrade, write an explanation and drop the homework
and the explanation into the
submission box near Gates 182/188.
Answers to commonly asked questions and clarifications
to the homeworks will be posted on the FAQ.