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.
Covers topics in multi-variable calculus and introduces differential equations, focusing on integral multivariable calculus and elementary ordinary differential equations.
An introduction to mathematical research. Instruction in ancillary skills such as literature review, mathematical software use, technical writing and communication, etc. Requires acceptance to the Madison Experimental Mathematics undergraduate research lab.
Applied Linear Algebra reviews matrix algebra, covering eigenvalues, diagonalization, and quadratic forms with numerical aspects and scientific applications.
Pouria Behnoudfar assigned homework unpredictably, sometimes with less than a week to complete, and exams were described as more difficult than the material taught in class, requiring significant self-study to succeed.
Students reported that the homework directly prepares them for exams, which were described as very straightforward. The class involved lecture-heavy instruction.
A rigorous, proof-based introduction to ordinary differential equations and dynamical systems, covering formal definitions, standard theorems, and mathematical argumentation.
Analysis I covers real numbers, set theory, metric spaces, basic topology, sequences and series, limits, continuity, differentiation, integration, and uniform convergence.
Yayi Fu covered a massive amount of material that could feel overwhelming. The final exam was worth 40% of the grade and was harder than the midterms, which were derived directly from review exercises.
Analysis II covers advanced real analysis topics such as special functions, power series, Fourier series, compactness, normed-space calculus, and the implicit and inverse function theorems.
One student reported that tests were extremely tough but fair, and homework was similarly challenging, recommending that students study the proofs and homework closely to succeed.
Homework varied drastically between manageable and really difficult, and one student felt the final exam was harder than past versions, though the midterm was not too bad.
Rigorous introduction to math for data science covering matrix factorizations, optimization, probabilistic models, and Markov chains with applications to machine learning.
A student said Hanbaek Lyu’s exam philosophy was to make exams pretty hard but grade them leniently, and the course felt like an extension of linear algebra with a focus on proofs.
Modern Algebra 2 covers field extensions, roots of polynomials, splitting fields, simple extensions, linear transformations, matrices, characteristic roots, canonical forms, and determinants.