GPA and percentages are weighted by recorded letter grades. UW
comparisons use the same terms. Cross-listed courses count once within
this department and once across UW; department totals should not be
added together. Grade counts are not unique students. Offering counts
reflect captured records, not a complete historical schedule.
Spring 2027
Recorded this term
8484courses with offerings
9191instructors recorded
Grades have not been recorded for this term. Historical results remain
in the overview above.
Introduction to cryptography covering secure transmission of digital information, distinguishing perfect from computational security, and introducing technical aspects.
Linear Optimization covers problems with linear inequality constraints, emphasizing formal proofs, the simplex method's theory, duality, and theorems of the alternatives.
Homeworks were extremely hard at times, though the total volume was limited to eight assignments. The workload is demanding due to the difficulty of the problems rather than the quantity.
Covers direct and iterative solutions for linear/nonlinear systems and eigenproblems, including LU/QR factorizations, least squares, and algorithmic stability.
One past student said Amos Ron’s homeworks were 10–15 pages biweekly. Another said the homework took many hours and lectures met three days a week for 75 minutes.
One student found the homework tedious but manageable, while another noted a heavy workload that was doable with time. Rycroft's exams were challenging but included partial credit, whereas Ohm's exams were described as having very reasonable difficulty.
Introduction to spectral methods (Fourier, Chebyshev, Fast Fourier Transform), finite element methods (Galerkin methods, energy estimates and error analysis), and mesh-free methods (Monte-Carlo, smoothed-particle hydrodynamics) for solving partial differential equations. Applications from science and engineering. Applications from science and engineering. Students are strongly encouraged to have programming skills (e.g.COMP SCI 200), undergraduate numerical analysis (e.g.MATH/COMP SCI 514orCOMP SCI 412), analysis (MATH 322and521) and linear algebra (e.g.MATH 341or equiv.)
The course required learning a large amount of material, with assignments strictly take-home and no in-class pressure. Reviewers noted the extensive prerequisite list made the content challenging before the semester even began.
Introduction to combinatorics covering enumeration, inclusion-exclusion, generating functions, graph coloring, finite designs, and matching problems with applications in science and puzzles.
The grading system was unique and demanding: students needed full marks on all assignments and tests or extra credit to achieve an A; otherwise, the highest possible grade was an AB.
Covers discrete-time and continuous-time Markov chains, Poisson point processes, and renewal processes, with applications to queueing, branching, and models in science, engineering, and business.
Homework assignments were challenging and time-consuming, but students noted there were only about ten. The homework was significantly harder than the exams, meaning thorough homework completion often reduced the need for additional exam study.
College Algebra covering properties of elementary functions, equations, inequalities, and graphs. Focuses on formulating, analyzing, solving, and interpreting mathematical and real-world problems to provide algebra skills for calculus.
The class featured hard tests, long online homework assignments, and content some students found overly complicated for a first-semester algebra class.
Covers the graphs, properties and geometric significance of trigonometric functions of a real variable. Other topics include trigonometric equations and identities, trigonometric form of complex numbers, DeMoivre's theorem, and polar and parametric equations. Formulating, analyzing, solving, and interpreting mathematical and real-world problems using trigonometry.
Attendance was mandatory due to weekly quizzes, with three dropped. Lectures were problem-heavy, and exam preparation materials were identical to the actual exams.
The course included weekly quizzes that students found easy if they studied, and exams were reported to be almost identical to the practice exams provided.
Quantitative Reasoning and Problem Solving develops competency in working with numerical data, solving quantitative problems in authentic contexts, and making logical arguments supported by quantitative evidence.
Survey of Calculus 1 covers differential and integral calculus, exponential/logarithmic functions, and functions of several variables for prebusiness and social science students.
The course was described as especially hard for students without prior calculus experience, as the instructor taught as if the material was already known.
Survey of Calculus 2 covers first-order differential equations, multivariable calculus, constrained optimization, infinite sequences and series, approximation methods, and probability, with models from business and social sciences.
One student with a pre-calculus background had to reteach themselves majority of concepts after lectures. Another found that discussions were the most helpful resource for learning material and studying for quizzes and tests.
The course was described as heavily exam-focused, requiring students to complete numerous practice tests, with some noting that self-study and extensive problem practice were essential for success.
A student taking Alexander Hanhart’s class found the homework took a lot of time to complete, though they felt the exams were not notably more difficult than previous semesters.
Techniques in Ordinary Differential Equations covering linear DEs, series solutions, boundary value problems, Laplace transforms, and possibly numerical methods and autonomous systems.
Past student reviews · Jessica Lin, Minh Binh Tran
Lectures sometimes moved quickly, making it easy to get lost. Exams had a heavy time crunch, though students noted that questions were often taken directly from homework or recommended problems.
Introduction to linear algebra and differential equations, emphasizing the connection between linear algebra theory and analytical/numerical techniques for solving differential equations.
The course involves very little homework, with three exams comprising almost the entire grade. One student noted a sharp difficulty curve early in the semester and strict grade cutoffs.
Covers Sturm-Liouville theory, Fourier series, and linear second-order PDEs including separation of variables, eigenfunction expansions, and Green's functions.
Introductory Probability covers axioms, random variables, distributions, expectation, variance, conditional probability, inequalities, laws of large numbers, and the central limit theorem.
Past student reviews · Autumn Kent, Kaitlyn Phillipson
No homework was collected in Kent’s section, with grades based on exams and quizzes. Phillipson’s section was described as having a fairly light workload.
Linear Algebra and Optimization introduces linear algebra, multivariable calculus, and optimization theory with applications to data science, implemented in Python.