Course title: Machine Learning

Course number: COMP.5450 (graduate), COMP.4220 (undergraduate)

Semester:  Spring 2017

Location:  Olsen Hall 503

Meeting Times: Tue., 5:30PM-8:15PM

Instructor: Dr. Jerome J. Braun,

TA: Xinzi Sun,

Course overview

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 a graduate/upper-level undergraduate course. All students should have be familiar with fundamentals of probability, linear algebra, and calculus.  All must also be familiar with algorithmic and programming techniques needed for the implementation of machine-learning algorithms that will be taught in this course.  

The above proficiencies are assumed typical for students who have successfully completed the following courses (or their equivalents):  Discrete Structures II (or Linear Algebra), Probability & Statistics I, Computing II.

Students enrolled in the graduate section  are expected to have proficiency in the areas of probability and linear algebra commensurate with their graduate status, and are assumed typical for students who have completed Linear Algebra I and II and Calculus III.  

Quiz 0 on prerequisite math knowledge (Probability & Statistics, Calculus, basic Matrix Algebra) will be given in the first week of class.   This quiz tests mathematical skills necessary for the course.  Moreover, this quiz is intended to help the students self-evaluate their prior knowledge of mathematical fundamentals expected of students taking this course.  In all cases, the responsibility to have or to acquire mathematical skills needed for this course shall rest with the students.


Required textbook:

Other recommended books:

Topics Overview (tentative)

Student Learning Outcomes

After successfully completing this course, the students should be able to understand:

These goals will be evaluated through quizzes, homework assigments, and a final project.

Deliverables/Graded work

Students will be evaluated based on the following graded work:

Graduate-section students are expected to achieve higher scores and/or submit additional work to receive the same grade as undergraduate-section students. 


Academic Honesty Policy:  Students are expected to honor and follow 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. Cheating will not be tolerated, and students who cheat risk failing the course and possible university administrative actions.  

All work on exams must be the student's own work.  

The work on homework assignments must also be the student's own work, with the following exceptions: 1) hints provided by the TA or the instructor may be used, provided that after obtaining such hints the students perform the assignment on their own, and that having obtained hints is acknowledged in writing in the student's work; 2) forming study-groups is allowed (and encouraged) and students may engage in discussions related to homework assignments, provided that following such discussions students complete the homework assignment separately on their own (without referring/copying detailed notes from those discussions) and that occurrence of such discussions is acknowledged in writing on the homework assignment (however, doing a homework assignment together by more than one person is not permitted).  Using homework solutions from any source, such as websites or past-year’s solutions obtained from any source, is not permitted. 

Violations may result in a warning, a possible grade of 0, and/or a report to the university.

Project work: The mandatory required final-project (see graded work section above) is expected to be done as teamwork performed by self-organized teams of, generally, up to three students enrolled in this course.  Accordingly, a collaborative work within these project teams on matters specific to their respective final project is permitted and required (all members of the project team must contribute).  Please note, however, that this permission applies to the above final-project work only; it does not apply to any other gradable items (such homework assignments, quizzes/exams).

Important notice regarding email: