Introduction to Machine Learning

10/21/11 |
Assignment 7 posted, due 11/20. |

10/21/11 |
Assignment 6 posted, due 11/9. |

10/28/11 |
Corrected Assignment 5 posted (removed requirement for fixed weights in 1(c) and (d)). |

10/21/11 |
Assignment 5 posted, due 10/30. |

10/14/11 |
Assignment 4 due date extended to due 10/23. |

10/14/11 |
Revised Assignment 4 posted (including testing code, new submission instructions), due 10/21. |

10/14/11 |
Assignment 4 posted (including training data), due 10/21. |

10/6/11 |
Midterm solutions posted. |

9/9/11 |
Assignment 0 solutions and Assignment 2 solutions posted. |

9/9/11 |
Assignment 3 posted, due 10/3. |

9/11/11 |
Corrected version of Assignment 2 posted (fixes typos in Q2 and Q4(c)). |

9/9/11 |
Assignment 2 posted (including training data), due 9/19. |

9/1ish/11 |
Assignment 1 posted, due 9/9. |

9/1/11 |
submit is not working, due to 194-10 being just a section of 194; for the time being, email your solutions
as firstname.lastname.tar.gz or firstname.lastname.zip to Avital at cs194-tc@imail.eecs.berkeley.edu. |

8/25/11 |
Corrected version of Assignment 0 posted, fixes typos in Q.3. |

8/22/11 |
Assignment 0 posted, due 9/2. |

8/16/11 |
Discussion sections WILL be held in Week 1, i.e., on Aug 24 before the first class; they will be in 310 Soda instead of the usual rooms. |

748 Sutardja Dai Hall, russell AT cs.berkeley.edu; (510) 642 4964

- Mert Pilanci, mert At eecs.berkeley.edu

Office Hours Tue 2-3pm and Wed 4-5pm, both in 751 Soda. - Avital Steinitz, steinitz AT eecs.berkeley.edu

Office Hours Wed 11-12 in 751 Soda.

101, Wed 10-11am, 75 Evans (Avital)

102, Wed 2-3pm, 3109 Etcheverry (Mert)

103, Wed 3-4pm, 87 Evans (Mert)

- Midterm (18%)
- Final (28%)
- Assignments (54% total) -- 9 assignments (A0 through A8, worth 2, 6, 6, 6, 6, 6, 6, 6, 10% respectively.),

Grading policy: the class is not graded on a curve. Grade is based on total percentage as follows:

A+ A A- B+ B B- C+ C C- D+ D D- F |
[90 -- 100]% [85 -- 90)% [80 -- 85)% [75 -- 80)% [70 -- 75)% [65 -- 70)% [60 -- 65)% [55 -- 60)% [50 -- 55)% [45 -- 50)% [40 -- 45)% [35 -- 40)% [0 -- 35)% |

These boundaries are sharp, i.e., no rounding up. Some assignments and exam questions may offer extra credit; good performance on extra credit questions may result in an improved grade, at the instructor's discretion.

A course grade of F will be assigned if the midterm or final is skipped.

- Trevor Hastie, Rob Tibshirani, and Jerry Friedman, Elements of Statistical Learning, Second Edition, Springer, 2009. (Full pdf available for download.)
- Kevin P. Murphy, Machine Learning: A Probabilistic Perspective. Unpublished. Access information will be provided.
- Stuart Russell and Peter Norvig,
*Artificial Intelligence: A Modern Approach*, Third Edition, Prentice Hall, 2010.

*The machine learning chapters were substantially revised in the third edition; previous editions are not usable for this course.* - Christopher Bishop, Pattern Recognition and Machine Learning, Springer, 2006.
- Ian Witten and Eibe Frank, Data Mining: Practical Machine Learning Tools and Techniques, Third Edition, Morgan Kaufmann, 2011.

Reading assignments for each week (to prepare for lecture, or review for assignments) appear here.

- Assignment 0, due 9/2, 2% of grade. Assignment 0 solutions.
- Assignment 1, due 9/9, 6% of grade. Assignment 1 solutions.
- Assignment 2, due 9/19, 6% of grade. Assignment 2 solutions.
- Assignment 3, due 10/3, 6% of grade. Assignment 3 solutions.
- Midterm, midterm solutions.
- Assignment 4, due 10/23, 6% of grade. Assignment 4 solutions.
- Assignment 5, due 10/30, 6% of grade. Assignment 5 solutions.
- Assignment 6, due 11/9, 6% of grade. Assignment 6 solutions.
- Assignment 7, due 11/20, 6% of grade. Assignment 7 solutions.

Each assignment will include a combination of problems to solve and programs to write and test.
Assignments should be turned in using the `submit` program
from an instructional (named or class) account, as described here.

If necessary, solutions to the homework problems can be turned in on paper
in the homework box in 283 Soda, or they may be turned
in online (e.g., as pdfs produced from LaTeX) using `submit`, as part of your overall submission.

*Except for Assignment 0, which must be done individually, assignments can be done individually or in pairs*. (This goes for both problem-solving and programming parts.)
If done in pairs, each partner should be involved in all the work!!
The usual rule about free-riding applies: the more you free-ride, the lower will be your score on the midterm and final.

Discussion of assignments among students is permitted and encouraged, but solutions
and programs may not be copied. I would recommend NOT mixing inter-group discussion
with writing up of solutions or code. See the
EECS Department Policy on Academic Dishonesty
and Kris Pister's policy for further explanation and examples.

**Finding solutions on the web**: It is becoming increasingly
difficult to give homework problems whose solutions are not already
available in some form on the web. This does not mean that your first
response to any homework is to type the question into Google. The EECS
policy begins "Copying all or part of another person's work, or *using
reference material not specifically allowed*, are forms of cheating."
For the purposes of this course, the allowed reference materials are
the reading materials listed on the course web page and any additional materials specified in the homework;
in addition, you may use Wikipedia for background reference.

It is a good idea to start your programming assignments as soon as you can; computers
have a tendency to go down the night before an assignment is due. There
is evidence from past courses that students who start working well
before the due date take about *one third* the
time to complete their work compared to students who wait until the last
minute. In general, it will be worth your while to spend more time away
from the screen thinking about programs than struggling with them
on-line.

The class newsgroup is suitable for asking general questions about what the homework questions mean, how the course software works, etc. Do not ask or answer specific questions about homework solutions, e.g., "What's the right answer for number 2?" One of the course GSIs will be checking the newsgroup fairly regularly, but for "official" answers to important questions you might want to email your own GSI directly, AFTER you have checked to see if the question has already been answered on the newsgroup!