This session has ended. For next session, see Machine Learning Spring 2015.
Title: Machine Learning
Course number: 91.422 (undergraduate) 91.545 (graduate)
Session: Spring 2014
Instructor: Kate Saenko, email@example.com
TA: Edward Luo, firstname.lastname@example.org
Location: Olsen Hall, Room 406
Meeting Times: Tue/Thu 4-5:15pm
Kate: Olsen 223, Mon 10:00-12:00, Tue 13:00-14:00, and by appointment
Edward: Olsen 212A, Tue/Thu 11:30-13:00
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 is an upper-level undergraduate/graduate course. Students should have completed the following courses (or their equivalents):
In addition, students must complete and pass Quiz 0 on prerequisite math knowledge – Probability & Statistics, Discrete math, Calculus, basic Matrix Algebra – given in the first week of class. Students who cannot pass Quiz 0 are strongly discouraged from taking the class.
the course can be taken as part of the two-course project sequence in combination with either 91.420 Artificial Intelligence, 91.530 Topics: Natural Language Processing, or 91.423 Computer Vision.
The required textbook is:
The course will also follow and build on Andrew Ng’s online course lectures, available at
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 quizzes, homeworks, and a final project.
Students will be evaluated based on the following graded work (subject to change):
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.
Important: if you cannot submit an assignment on time because of CS department server issues, please IMMEDIATELY 1) send an email to email@example.com, 2) cc: the instructor and TA, 3) do not modify the timestamps on your files, i.e. timestamps should be from before the deadline, even if the files are submitted later. Only doing (1)-(3) can result in late charge reversal.
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.