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Class Hours
Lecture: MW 4-5:15pm - Keller 3-230. Some activities will be semi-synchronous.
Seating is limited by available staff support, which has yet to be determined, as well as room size.
Textbook
Introduction to Machine Learning; by Ethem Alpaydin (3rd ed, 2014, or 4th ed, 2020?).
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(above link requires UofM login or on campus or VPN.)
Instructor: Prof. Daniel Boley,
Office: Keller 4-225C
Phone: 612-625-3887 (lv message)
Office Hours: MW 5:15-6:15pm (after class)
office hours are subject to change after the first two weeks
To avoid my e-mail spam filter, please include
the string "5521" in the subject line.
TA: Hunmin Lee
Email: lee03915-a-umn.edu
Office Hours and Location: ZOOM: See Canvas page under General information.
TA: Ivan Radkevich
Email: radke149-a-umn.edu
Office Hours and Location: ZOOM: See Canvas page under General information.
Assignment Plan (not in order)
If the number of exams and/or assignments changes, the relative weights will
be adjusted.
General Information
Neural networks, non-parametric windowing, and Bayes statistical
theory are three popular methods for recognizing and classifying
patterns - the process of Pattern Recognition.
These are the basic machine learning algorithms applicable to
high-dimensional numerical data.
We introduce the
fundamental concepts of these various approaches, including the
classification phase and the learning phase. Part of the class
will be devoted to methods for unsupervised learning and
classification. We assume just some knowledge of elementary
statistics, calculus, and elementary linear algebra at the upper
division undergraduate level, plus programming experience in python. A combination of written
assignments and programming projects will be used to
illustrate the concepts. All programming will be done in
python.
TOPICS by week
Students will be expected to implement several of the algorithms on a digital computer in python. Exact details will be forthcoming before the semester starts. In some cases, you will be provided with a skeleton code and will be asked to fill in the gaps, which will be mostly related to the mathematical foundations of the methods being implemented. Students should be familiar with basic programming techniques, as well as being able use the help system python. Students should also be acquainted with the basic concepts in linear algebra and probability, though some of this will be reviewed during the course.
All items handed in to be graded must represent the individual effort of whoever's name(s) appears on the item. At a minimum, violators of this policy will fail the course and/or will have their names recorded at the appropriate University or Department office. Mutual discussion of each individual's results in the homeworks is encouraged, as long as the results themselves represent individual efforts. To pass the course, you will have to achieve a passing grade on the exams alone, and do satisfactorily on the homeworks. After any graded item is first handed back, you have at most one week to ask about the way it was graded.
You should also be aware of the following University-wide policies:
Late submissions: Homeworks will be accepted up to two working days, but before any answers are discussed in class or posted. Short-turnaround assignment (like take-home exams) can be accepted only up to one day late before any answers are posted. The late penalty of 1 point per day late per assignment (2 points for the short-turnaround assignments) shall be applied to the final total score at the end of the semester, not to each individual assignment. There will be an allowance: the first 4 penalty points accumulated during the semester shall be waived with no questions. Classroom exercises will be handed out during the posted class time on an irregular basis and should be submitted electronically through canvas within 24 hours. Students signing up for the class during the first two weeks of the semester must hand in missed classroom exercises during the day of their first attendance. These deadlines may be extended for excused absences on a case-by-case basis, but only for a limited number of occurences.