BIOINF 547 - Mathematics of Data
Winter 2022, Section 001
Instruction Mode: Section 001 is  In Person (see other Sections below)
Subject: Bioinformatics and Computational Biology (BIOINF)
Department: MED Bioinformatics
See additional student enrollment and course instructor information to guide you in your decision making.


Requirements & Distribution:
Waitlist Capacity:
Advisory Prerequisites:
MATH, Flexible, due to diverse backgrounds of intended audience. Basic probability (level of MATH/STATS 425), or molecular biology (level of BIOLOGY 427), or biochemistry (level of CHEM/BIOLCHEM 451), or basic programming skills desirable or permission.
This course counts toward the 60 credits of math/science required for a Bachelor of Science degree.
May not be repeated for credit.
Primary Instructor:


This course is open to graduate students and upper-level undergraduates in applied mathematics, bioinformatics, statistics, and engineering, who are interested in learning from data. Students with other backgrounds such as life sciences are also welcome, provided they have maturity in mathematics. The mathematical content in this course will be linear algebra, multilinear algebra, dynamical systems, and information theory. This content is required to understand some common algorithms in data science. I will start with a very basic introduction to data representation as vectors, matrices, and tensors. Then I will teach geometric methods for dimension reduction, also known as manifold learning (e.g. diffusion maps, t-distributed stochastic neighbor embedding (t-SNE), etc.), and topological data reduction (introduction to computational homology groups, etc.). I will bring an application-based approach to spectral graph theory, addressing the combinatorial meaning of eigenvalues and eigenvectors of their associated graph matrices and extensions to hypergraphs via tensors. I will also provide an introduction to the application of dynamical systems theory to data including dynamic mode decomposition. Real data examples will be given where possible and I will work with you write code implementing these algorithms to solve these problems. The methods discussed in this class are shown primarily for biological data, but are useful in handling data across many fields. A course features several guest lectures from industry and government.

There is no textbook for this course.

For more information on this course, please visit the Department of Mathematics webpage


BIOINF 547 - Mathematics of Data
Schedule Listing
001 (LEC)
 In Person
TuTh 2:30PM - 4:00PM

Textbooks/Other Materials

The partner U-M / Barnes & Noble Education textbook website is the official way for U-M students to view their upcoming textbook or course material needs, whether they choose to buy from Barnes & Noble Education or not. Students also can view a customized list of their specific textbook needs by clicking a "View/Buy Textbooks" link in their course schedule in Wolverine Access.

Click the button below to view and buy textbooks for BIOINF 547.001

View/Buy Textbooks


Syllabi are available to current LSA students. IMPORTANT: These syllabi are provided to give students a general idea about the courses, as offered by LSA departments and programs in prior academic terms. The syllabi do not necessarily reflect the assignments, sequence of course materials, and/or course expectations that the faculty and departments/programs have for these same courses in the current and/or future terms.

No Syllabi are on file for BIOINF 547. Click the button below to search for a different syllabus (UM login required)

Search for Syllabus

CourseProfile (Atlas)

The Atlas system, developed by the Center for Academic Innovation, provides additional information about: course enrollments; academic terms and instructors; student academic profiles (school/college, majors), and previous, concurrent, and subsequent course enrollments.

CourseProfile (Atlas)