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48 courses
Ranked courses have at least 100 recorded letter grades.
Introduction to ergodic theory and dynamics covering fundamental theorems, classical examples of one and two dimensional dynamics, and applications to group actions.
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.
Abstract Algebra continuation covering commutative rings and fields, including ring-theoretic and field-theoretic topics such as Galois theory and the Hilbert Nullstellensatz.
Covers topics in multi-variable calculus and introduces differential equations, focusing on integral multivariable calculus and elementary ordinary differential equations.
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.
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.
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.
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.
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.
Homework assignments can be challenging and may rely heavily on statistical software like R, which some students found difficult without prior experience. The course material itself is generally not considered overly difficult conceptually.
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.
Modern Algebra 2 covers field extensions, roots of polynomials, splitting fields, simple extensions, linear transformations, matrices, characteristic roots, canonical forms, and determinants.
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.
Introduction to Probability and Mathematical Statistics I covers probability theory, combinatorial methods, discrete and continuous distributions, moments, and estimation.
One student found exams crazy difficult and felt the instructor assumed students had taken analysis. Another said open-book exams required high mathematical skills.
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.
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.
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.
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.
Introduction to cryptography covering secure transmission of digital information, distinguishing perfect from computational security, and introducing technical aspects.
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.
Students reported that the homework directly prepares them for exams, which were described as very straightforward. The class involved lecture-heavy instruction.