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Your search for courses · during 25SP · tagged with MATH Electives · returned 9 results

CS 252 Algorithms 6 credits
A course on techniques used in the design and analysis of efficient algorithms. We will cover several major algorithmic design paradigms (greedy algorithms, dynamic programming, divide and conquer, and network flow). Along the way, we will explore the application of these techniques to a variety of domains (natural language processing, economics, computational biology, and data mining, for example). As time permits, we will include supplementary topics like randomized algorithms, advanced data structures, and amortized analysis.
 Spring 2025
 FSR, Formal or Statistical Reasoning

Student has completed any of the following course(s): CS 200 – Data Structures with Problem Solving or CS 201 – Data Structures AND CS 202 – Mathematics of Computer Science or MATH 236 – Mathematical Structures with a grade of C or better or equivalent. MATH 236 will be accepted in lieu of Computer Science 202.

CS 252.00 Spring 2025
 Faculty:Eric Alexander 🏫 👤
 Size:34
 M, WHulings 316 9:50am11:00am
 FHulings 316 9:40am10:40am

CS 254 Computability and Complexity 6 credits
An introduction to the theory of computation. What problems can and cannot be solved efficiently by computers? What problems cannot be solved by computers, period? Topics include formal models of computation, including finitestate automata, pushdown automata, and Turing machines; formal languages, including regular expressions and contextfree grammars; computability and uncomputability; and computational complexity, particularly NPcompleteness.
 Spring 2025
 FSR, Formal or Statistical Reasoning

Student has completed any of the following course(s): CS 200 – Data Structures with Problem Solving or CS 201 – Data Structures AND CS 202 – Mathematics of Computer Science or MATH 236 – Mathematical Structures with a grade of C or better or equivalent. MATH 236 will be accepted in lieu of Computer Science 202.

CS 254.00 Spring 2025
 Faculty:Layla Oesper 🏫 👤
 Size:34
 M, WHulings 316 12:30pm1:40pm
 FHulings 316 1:10pm2:10pm

MATH 241 Ordinary Differential Equations 6 credits
Ordinary differential equations are a fundamental language used by mathematicians, scientists, and engineers to describe processes involving continuous change. In this course we develop ordinary differential equations as models of real world phenomena and explore the mathematical ideas that arise within these models. Topics include separation of variables; phase portraits; equilibria and their stability; nondimensionalization; bifurcation analysis; and modeling of physical, biological, chemical, and social processes.
 Spring 2025
 FSR, Formal or Statistical Reasoning

Student must have completed any of the following course(s): MATH 134 – Linear Algebra with Applications or MATH 232 – Linear Algebra AND MATH 120 – Calculus 2 or MATH 211 – Multivariable Calculus with a grade of C or better or equivalents.

MATH 271 Optimization 6 credits
Optimization is all about selecting the "best" thing. Finding the most likely strategy to win a game, the route that gets you there the fastest, or the curve that most closely fits given data are all examples of optimization problems. In this course we study linear optimization (also known as linear programming), the simplex method, and duality from both a theoretical and a computational perspective. Applications will be selected from statistics, economics, computer science, and more. Additional topics in nonlinear and convex optimization will be covered as time permits.
 Spring 2025
 FSR, Formal or Statistical Reasoning

Student must have completed any of the following course(s): MATH 134 – Linear Algebra with Applications or MATH 232 – Linear Algebra AND MATH 120 – Calculus 2 or MATH 211 – Multivariable Calculus with a grade of C or better or equivalents.

MATH 331 Real Analysis II 6 credits
Further topics in analysis such as measure theory, Lebesgue integration or Banach and Hilbert spaces.
 Spring 2025
 FSR, Formal or Statistical Reasoning

Student has completed any of the following course(s): MATH 321 – Real Analysis I with a grade of C or better.

MATH 341 Partial Differential Equations 6 credits
An introduction to partial differential equations with emphasis on the heat equation, wave equation, and Laplace’s equation. Topics include the method of characteristics, separation of variables, Fourier series, Fourier transforms and existence/uniqueness of solutions.
 Spring 2025
 FSR, Formal or Statistical Reasoning

Student has completed any of the following course(s): MATH 241 – Ordinary Differential Equations with grade of C or better.

MATH 342 Abstract Algebra I 6 credits
Introduction to algebraic structures, including groups, rings, and fields. Homomorphisms and quotient structures, polynomials, unique factorization. Other topics may include applications such as Burnside’s counting theorem, symmetry groups, polynomial equations, or geometric constructions.
 Spring 2025
 FSR, Formal or Statistical Reasoning

Student has completed any of the following course(s): MATH 236 – Mathematical Structures with a grade of C or better or equivalent.

STAT 250 Introduction to Statistical Inference 6 credits
Introduction to modern mathematical statistics. The mathematics underlying fundamental statistical concepts will be covered as well as applications of these ideas to reallife data. Topics include: resampling methods (permutation tests, bootstrap intervals), classical methods (parametric hypothesis tests and confidence intervals), parameter estimation, goodnessoffit tests, regression, and Bayesian methods. The statistical package R will be used to analyze data sets.
 Spring 2025
 FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning

Student has completed any of the following course(s): MATH 240 – Probability with a grade of C or better.

STAT 320 Time Series Analysis 6 credits
Models and methods for characterizing dependence in data that are ordered in time. Emphasis on univariate, quantitative data observed over evenly spaced intervals. Topics include perspectives from both the time domain (e.g., autoregressive and moving average models, and their extensions) and the frequency domain (e.g., periodogram smoothing and parametric models for the spectral density). Exposure to matrix algebra may be helpful but is not required.
 Spring 2025
 FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning

Student has completed any of the following course(s): STAT 230 – Applied Regression Analysis and STAT 250 – Introduction to Statistical Inference with a grade of C or better.