## ENROLL Course Search

**NOTE:** There are some inconsistencies in the course listing data - ITS is looking into the cause.

**Alternatives:** For requirement lists, please refer to the current catalog. For up-to-the-minute enrollment information, use the "Search for Classes" option in The Hub. If you have any other questions, please email registrar@carleton.edu.

Your search for courses for 23/SP and with code: MATHELEC found 9 courses.

### CS 252.00 Algorithms 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

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11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |

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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.

*Prerequisite:* Computer Science 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)

### CS 254.00 Computability and Complexity 6 credits

Open: Size: 34, Registered: 0, Waitlist: 0

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9:50am11:00am | 9:50am11:00am | 9:40am10:40am |

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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 finite-state automata, pushdown automata, and Turing machines; formal languages, including regular expressions and context-free grammars; computability and uncomputability; and computational complexity, particularly NP-completeness.

*Prerequisite:* Computer Science 201 and Computer Science 202 (Mathematics 236 will be accepted in lieu of Computer Science 202)

### MATH 241.00 Ordinary Differential Equations 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

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1:50pm3:00pm | 1:50pm3:00pm | 2:20pm3:20pm |

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*Prerequisite:* Mathematics 232 or instructor permission

### MATH 261.00 Functions of a Complex Variable 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

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*Prerequisite:* Mathematics 210 or Mathematics 211

### MATH 331.00 Real Analysis II 6 credits

Open: Size: 20, Registered: 0, Waitlist: 0

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1:50pm3:00pm | 1:50pm3:00pm | 2:20pm3:20pm |

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*Prerequisite:* Mathematics 321 or instructor permission

### MATH 341.00 Partial Differential Equations 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

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11:10am12:20pm | 11:10am12:20pm | 12:00pm1:00pm |

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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.

*Prerequisite:* Mathematics 241

### MATH 342.00 Abstract Algebra I 6 credits

Open: Size: 25, Registered: 0, Waitlist: 0

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9:50am11:00am | 9:50am11:00am | 9:40am10:40am |

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*Prerequisite:* Mathematics 236 or instructor permission

### MATH 395.00 Introduction to Analytic Number Theory 6 credits

Open: Size: 18, Registered: 0, Waitlist: 0

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1:15pm3:00pm | 1:15pm3:00pm |

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Caroline L Turnage-Butterbaugh

An introduction to the techniques and principles of analytic number theory. Topics covered include arithmetical functions, Dirichlet multiplication, averages of arithmetical functions, elementary theorems on the distribution of the primes, and Dirichlet's theorem on primes in arithmetic progressions.

*Prerequisite:* Mathematics 282 (formerly MATH 312) or Mathematics 342

### STAT 250.00 Introduction to Statistical Inference 6 credits

Open: Size: 30, Registered: 0, Waitlist: 0

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12:30pm1:40pm | 12:30pm1:40pm | 1:10pm2:10pm |

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(Formerly Mathematics 275) Introduction to modern mathematical statistics. The mathematics underlying fundamental statistical concepts will be covered as well as applications of these ideas to real-life data. Topics include: resampling methods (permutation tests, bootstrap intervals), classical methods (parametric hypothesis tests and confidence intervals), parameter estimation, goodness-of-fit tests, regression, and Bayesian methods. The statistical package R will be used to analyze data sets.

*Prerequisite:* Mathematics 240 Probability (formerly Mathematics 265)

Formerly Mathematics 275

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