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Title: Machine Learning
Course number: 91.545 (graduate) TBD (undergraduate)
Instructor: Kate Saenko, firstname.lastname@example.org
TA: Baochen Sun, email@example.com
Location: Olsen Hall, Room 401
Meeting Times: TR 12:30-13:45
Kate: TR 13:45-15:15(after class) Olsen 223
Baochen: M 11:00-14:00 Olsen 212A
This introductory course gives an overview of machine learning techniques used in data mining and pattern recognition applications. Topics include: foundations of machine learning, including statistical and probabilistic methods; generative and discriminative models; linear regression; Bayesian methods; parametric and non-parametric classification; supervised and unsupervised learning; clustering and dimensionality reduction; anomaly detection; and applications to very large datasets.
This course does not require a textbook, but will instead follow and build on Andrew Ng’s online course lectures, available at
The main recommended supplemental textbook is:
Other recommended supplemental textbooks are:
Software and Hardware
The course will use the Octave scientific programming environment, a free version of Matlab. To facilitate programming assignments, software libraries in the corresponding programming language will be made available to the students.
The class will meet twice a week for a 75 min lecture, taught by the instructor.
Student Learning Outcomes
After the completion of the course, the students should be able to understand
These goals will be evaluated through the in-class quizzes, biweekly homeworks, a midterm exam, and a final project.
Students will be evaluated based on the following graded work:
Students enrolled in the graduate section will be expected to submit additional work and/or achieve a higher percentage on the exams to receive the same grade as undergraduate section students.
This is an upper-level undergraduate/graduate course. Students should have completed the following courses (or their equivalents):
In addition, the course can be taken as part of a project sequence in combination with 91.420 Artificial Intelligence.
Academic Honesty Policy: Students are expected to honor all CS department and UMass Lowell policies related to academic honesty and integrity. Violators risk failing the course in addition to any actions taken by university administration. The default CS department policy is that a student who cheats will fail the course. The University's policy is described here. A definition of plagiarism is here. All work on exams must be the student's own work. All work on homework assignments must also be the student's own work, with the following exceptions: 1) hints provided by the instructor or TA may be used but must be acknowledged in writing in the student's work; 2) high-level hints from another student may also be used if a student is unable to make progress on an assignment problem on his/her own; this type of hint must also be acknowledged in writing in the student's work. Detailed collaboration among students on homework assignments is not permitted. Students cannot obtain homework answers from web sites.
Religious Observance: UMass Lowell respects the religous observances of students. If religious obligations conflict with course due dates and/or examinations, students should notify the professor in writing well in advance of the due date.