Benjamin Marlin

Course Description: This course will introduce core machine learning models and algorithms for classification, regression, clustering, and dimensionality reduction. On the theory side, the course will focus on effectively using machine learning methods to solve real-world problems with an emphasis on model selection, regularization, and empirical evaluation. The assignments will involve both mathematical problems and implementation tasks. The course will assume experience with the Python programming language. While this course has an applied focus, it still requires appropriate mathematical background in probability and statistics, calculus, and linear algebra. This course counts as a CS Elective for the CS Major. 3credits.

• Date/Time: TuTh 10:00AM – 11:15AM
• Location: Goessmann Laboratory 64

Wait list, Alternative Prerequisites, and Overrides:

• Wait list for CS students: For UMass CS undergraduate students meeting prerequisites (see below) and for UMass CS grad students, if the class fills, you may try to get a spot on the Spire course wait list. If the Spire course wait list fills, you will need to check back until a spot opens on the wait list or in the course. Please do not submit an override when the course fills.
• Alternative Prerequisites: Undergraduate students who have completed equivalent prerequisites at UMass or at other institutions should use the Alternative Prerequisites process as a first step toward requesting a seat in the course. Note that the course already supports multiple prerequisite options for UMass CS undergrads. It is expected that UMass CS undergrads will only be granted exceptions to the COMPSCI 589 course prerequisites when replacing a required prerequisite course with a more advanced course (e.g., STATISTIC 607 in place of STATISTIC 315/515).  
• Overrides: Undergraduate students from outside UMass CS (including Five College students) who meet the specific UMass prerequisites listed below and graduate students from outside UMass CS need to submit an override request to enroll in the course. More information on the override process is available here. Undergraduate students from outside UMass CS (including Five College students) who have alternatives to the stated prerequisites should follow the alternative prerequisites process described above.

Undergraduate Prerequisites: MATH 545 and COMPSCI 240 and STATISTC 315/515 all with a grade of C or better. (MATH 545 can be skipped by students who have taken MATH 235 and MATH 233 both with B+ or better. STATISTC 315/515 can be skipped by students who have taken COMPSCI 240 with a B+ or better).

Graduate Student Preparation: Strong foundations in linear algebra, calculus, probability, and statistics are essential for successfully completing this course. Graduate students from outside computer science with sufficient background are also welcome to take the course. Graduate students should check the descriptions for these courses to verify that they have sufficient mathematical background for 589.

Textbooks: The course will only use text books that are available free to UMass students either as ebooks through the UMass library or as open texts available online. Sources will include

• The Elements of Statistical Learning, by Hastie, Tibshirani and Friedman.
• An Introduction to Statistical Learning, by James, Witten, Hastie and Tibshirani
• Pattern Recognition and Machine Learning, by Chris Bishop

Computing: Students will be able to complete assignments on a moderately recent laptop. The use of Python as a programming language is required. Students can also make use of free services such as Google Colab.

COMPSCI H589 and Departmental Honors: Undergraduate students who want to receive departmental honors credit for 589 will also need to register for the associated 1-credit honors colloquium, COMPSCI H589.

Graduate Students from Other Departments: Graduate students from outside computer science with sufficient background are welcome to take the course. Graduate students from outside CS will need to request a course override. Override requests will be granted pending availability of seats in the course and evidence that students are sufficiently prepared to be successful in the course.

Website: The website for this course will be hosted on the Canvas LMS.