Search Results
Your search for courses · during 25SP · tagged with SDSC CS Elective · returned 4 results
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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
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Student has completed any of the following course(s): CS 200 with a grade of C- or better or CS 201 with a grade of C- or better or received a Carleton Computer Science 200 Requisite Equivalency AND CS 202 with a grade of C- or better or received a Carleton Computer Science 202 Requisite Equivalency or MATH 236 with a grade of C- or better or received a Carleton Math 236 Requisite Equivalency. MATH 236 will be accepted in lieu of CS 202.
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CS 252.00 Spring 2025
- Faculty:Eric Alexander 🏫 👤
- Size:34
- M, WLanguage & Dining Center 104 9:50am-11:00am
- FLanguage & Dining Center 104 9:40am-10:40am
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34 – reserved for REQ: CS 252 Match (Condition Rule) until 3/5/2025
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CS 257 Software Design 6 credits
It’s easy to write a mediocre computer program, and lots of people do it. Good programs are quite a bit harder to write, and are correspondingly less common. In this course, we will study techniques, tools, and habits that will improve your chances of writing good software. While working on several medium-sized programming projects, we will investigate code construction techniques, debugging and profiling tools, testing methodologies, UML, principles of object-oriented design, design patterns, and user interface design.
- Spring 2025
- FSR, Formal or Statistical Reasoning
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Student has completed any of the following course(s): CS 200 or CS 201 with a grade of C- or better or received a Carleton Computer Science 201 or better Requisite Equivalency.
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CS 257.01 Spring 2025
- Faculty:Jeff Ondich 🏫 👤
- Size:28
- M, WAnderson Hall 329 8:30am-9:40am
- FAnderson Hall 329 8:30am-9:30am
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12 – reserved for REQ: CS 257 Match (Condition Rule) until 3/5/2025
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CS 257.02 Spring 2025
- Faculty:Anya Vostinar 🏫 👤
- Size:28
- M, WAnderson Hall 329 11:10am-12:20pm
- FAnderson Hall 329 12:00pm-1:00pm
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22 – reserved for REQ: CS 257 Match (Condition Rule) until 3/7/2025
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CS 320 Machine Learning 6 credits
What does it mean for a machine to learn? Much of modern machine learning focuses on identifying patterns in large datasets and using these patterns to make predictions about the future. Machine learning has impacted a diverse array of applications and fields, from scientific discovery to healthcare to education. In this artificial intelligence-related course, we’ll both explore a variety of machine learning algorithms in different application areas, taking both theoretical and practical perspectives, and discuss impacts and ethical implications of machine learning more broadly. Topics may vary, but typically focus on regression and classification algorithms, including neural networks.
X seats held for CS Match until the day after X priority registration.
- Spring 2025
- FSR, Formal or Statistical Reasoning
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Student has completed any of the following course(s): CS 200 with a grade of C- or better or CS 201 with a grade of C- or better or received a Carleton Computer Science 200 Requisite Equivalency AND CS 202 with a grade of C- or better or received a Carleton Computer Science 202 Requisite Equivalency or MATH 236 with a grade of C- or better or received a Carleton Math 236 Requisite Equivalency. MATH 236 will be accepted in lieu of CS 202.
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CS 320.00 Spring 2025
- Faculty:Tom Finzell 🏫
- Size:34
- M, WLanguage & Dining Center 104 11:10am-12:20pm
- FLanguage & Dining Center 104 12:00pm-1:00pm
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19 – reserved for REQ: CS 320 Match (Condition Rule) until 3/7/2025
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CS 348 Parallel and Distributed Computing 6 credits
As multi-core machines become more prevalent, different programming paradigms have emerged for harnessing extra processors for better performance. This course explores parallel computation for both shared memory and distributed parallel programming paradigms. In particular, we will explore how these paradigms affect the code we write, the libraries we use, and the advantages and disadvantages of each. Topics will include synchronization primitives across these models for parallel execution, debugging concurrent programs, fork/join parallelism, example parallel algorithms, computational complexity and performance considerations, computer architecture as it relates to parallel computation, and related theory topics.
- Spring 2025
- FSR, Formal or Statistical Reasoning
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Student has completed any of the following course(s): CS 200 or CS 201 with a grade of C- or better or received a Carleton Computer Science 201 or better Requisite Equivalency.
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CS 348.00 Spring 2025
- Faculty:David Musicant 🏫 👤
- Size:34
- M, WAnderson Hall 329 1:50pm-3:00pm
- FAnderson Hall 329 2:20pm-3:20pm
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21 – reserved for REQ: CS 348 Match (Condition Rule) until 3/7/2025