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Syllabus
CSci 5525: Machine Learning
3 Credits
Mon,Wed 04:00 P.M. - 05:15 P.M., EE/CSci 3-111
Fall 2008
Instructor:
Arindam Banerjee
EE/CS 6-213
banerjee AT cs dot umn.edu
Office Hours: M,W 05:15 P.M. - 6:15 P.M., or by appointment, in EE/CS 6-213
TA:
Amrudin Agovic
aagovic AT cs dot umn.edu
Office Hours: W 10am-12noon, EE/CS 2-209
If you want to speak with us about the
course and can't make one of the scheduled times, please send us email
and we'll try to schedule a meeting.
Textbooks:
The required textbook for this course is Pattern Recognition and Machine Learning, by Christopher M. Bishop.
We will also use supplementary material from other books, and lecture/technicalnotes. Other recommended/related books include
(i) The Elements of Statistical
Learning: Data Mining, Inference, and Prediction, by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, (ii) A Probabilistic Theory
of Pattern Recognition, by Luc Devroye, Laszlo Gyorfi, Gabor Lugosi, and
(iii) Machine Learning, by Tom Mitchell.
Grading:
Grading for this course will be based on the following
components:
- Four homeworks (50%, 12.5% each).
- Midterm exam (20%).
- Final exam (25%).
- Class participation (5%).
Grading is absolute (i.e. not on a curve). Grading will be as follows:
A = 90-100, A- = 80-90, B+ = 75-80, B = 65-75, B- = 60-65, C+ = 55-60,
C = 45-55, C- = 40-45, D+ = 30-40, D = 20-30, F = less than 20. If you are registered
S/N, then: S = 60-100, N = less than 60.
Late Submission Policy:
Late submissions will be penalized using the following rule:
- Late by 0-24 hrs: 25% deducted from actual score.
- Late by 24-48 hrs: 50% deducted from actual score.
- Late by 48-72 hrs: 75% deducted from actual score.
- Late by more than 72 hrs: Will receive a zero.
Exam Policy:
The midterm exam will be closed book/notes, but you are allowed to bring in notes on one standard sized page (the letter sized printer paper).
The final exam will be closed book/notes, but you are allowed to bring in notes on two standard sized pages.
Academic Integrity Policy:
This class will feature four homeworks. Students are encouraged to
discuss homework exercises with each other, but each student must turn
in his or her own, independent writeup of the solutions.
The University Student Conduct Code defines scholastic dishonesty as:
submission of false records of academic achievement; cheating on
assignments or examinations; plagiarizing; altering, forging, or
misusing a University academic record; taking, acquiring, or using test
materials without faculty permission; acting alone or in cooperation
with another to falsify records or to obtain dishonestly grades,
honors, awards, or professional endorsement. In this course, a student
responsible for scholastic dishonesty will be assigned a penalty of an
"F" or "N" for the course. If you have any questions regarding the
expectations for a specific assignment or
exam, ask.
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