CS 229: Machine Learning (Course Information)

CS 229
Machine Learning
Handout #1: Course Information

Meeting Times and Locations

Lectures: MW 9:30AM-10:45AM. Huang Aud.

Discussion sections: Fridays, 2:15-3:05pm. Location: Huang Aud (optional attendance).

Teaching Staff

Professor: Andrew Ng Office: Gates 156 Fax: (650)725-1449
Head TA: Richard Socher www.socher.org
TA: Marius Catalin Iordan
TA: David Kamm
TA: Abhik Lahiri
TA: Andrew Maas
TA: Peter Pham
TA: Tao Wang

Contact Information

If you are taking the free online class and your question is not answered on the website www.ml-class.org, you can reach the online class staff at ml-class@cs.stanford.edu.

If you are enrolled in CS229 at Stanford (i.e., you're a Stanford student or SCPD student) and have a question about a homework or a technical question about the class, we encourage you to post it on our forum on Piazza. To contact the CS229 teaching staff directly, you can also email us at cs229-qa@cs.stanford.edu. For telephone numbers and information about office hours (where we can help you directly), see Office Hours and Contact Information

Course Description

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 processing.

Prerequisites

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.

Familiarity with the basic probability theory. (CS109 or Stat116 is sufficient but not necessary.)

Familiarity with the basic linear algebra (any one of Math 51, Math 103, Math 113, or CS 205 would be much more than necessary.)

Course Materials

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

Online Resources

Home page: http://cs229.stanford.edu/
Current quarter's class videos: Available from SCPD
Piazza forum: http://piazza.com/class#fall2011/cs229
Staff mailing list: cs229-qa@cs.stanford.edu (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.

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 here. 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 cs229-qa@cs.stanford.edu . 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 Wednesday 11/9 at 6-9pm, in 320-105 and Cemex Auditorium.

Course grades: will be based 40% on homeworks (10% each), 20% on the midterm, and 40% on the major term project. Up to 3% extra credit may be awarded for class participation.

Sections

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 about a homework or assignment, we 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, cs229-qa@cs.stanford.edu (consisting of the TAs and the professor). By having questions sent to all of us, you will get answers much more quickly. Of course, more 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.

Comments to cs229-qa@cs.stanford.edu.

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