Introduction to Optimization covers mathematical optimization from a modeling and solution perspective, focusing on formulating discrete and continuous optimization problems and equilibrium models.
Past student reviews · Michael Ferris, Laurent Lessard
Homework assignments were difficult and time-consuming, with one student noting they would ruin their social life, while another found them helpful for learning.
Introduction to cryptography covering secure transmission of digital information, distinguishing perfect from computational security, and introducing technical aspects.
Matrix Methods in Machine Learning covers linear algebraic foundations including SVD, regression, and regularization applied to classification, clustering, and neural networks.
Introduction to Artificial Neural Networks covering theory and applications including MLPs, CNNs, RNNs, SVMs, and genetic algorithms for control, pattern recognition, and object detection.
The course had a huge workload with many homeworks, exercises, and a group project. The first half was very difficult without a strong background in Linear Algebra or Calc 3.
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.
Introduction to Computing provides a broad overview of computing, security, robotics, and AI, focusing on how computers work and algorithms solve problems via creative applications in a visual coding environment.
Students in Jeff Nyhoff's class said the material was fairly simple and that showing up to lecture, following along, and doing homework were enough to earn high grades.
Introduction to Web Development covers secure/interactive web content, programming fundamentals, CMSs, accessibility, design, ethics, and website implementation using scripting languages and frameworks.
Undergraduate Cooperative Education offering full-time work experience combining classroom theory with practical knowledge in computer science, data science, statistics, or information science.
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.
Human Resources Development: Career Strategies explores vocational development through didactic and experiential learning, focusing on self-assessment, the world of work, and career planning.
The Wisconsin Experience Seminar examines the transition to UW-Madison by exploring the research university and the Wisconsin Experience using texts for discussion, writing, and experiential assignments.
Introduction to the intersectionality framework in the U.S. focused on culturally responsive awareness, critical perspective-taking, and service learning preparation.
Students reported a significant reading load and noted that Dr. Gloria was nit-picky about assignments, deducting points for minor organizational issues. The workload was generally manageable if students provided detailed responses.
Explores children's psychological experience of racial, ethnic, and cultural status, their understanding development, and implications for working with REC diversity in educational contexts.
Mental Health, Self-Awareness, and Social Justice: Working in Diverse Communities increases knowledge and skills for addressing mental health within diverse identity groups and communities.
Theoretical and research framework for understanding suicide, covering myths, assessment, intervention, prevention, bereavement, ethical/legal issues, and affected populations.
An academic and experiential introduction to mindfulness meditation in psychology, covering MBSR, lovingkindness, and neuroscientific/clinical effects.
Explores school-based supports and one-on-one mentoring for young homeless children to foster resilience, covering homelessness, mentoring, and emotionally responsive teaching.