Modify Your Search
Search Results
Your search for courses · during 25FA, 26WI, 26SP · taught by annameyer · returned 4 results
-
CS 111 Introduction to Computer Science 6 credits
This course will introduce you to computer programming and the design of algorithms. By writing programs to solve problems in areas such as image processing, text processing, and simple games, you will learn about recursive and iterative algorithms, complexity analysis, graphics, data representation, software engineering, and object-oriented design. No previous programming experience is necessary.
- Spring 2026
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
-
NOT open to students who have completed any of the following course(s): CS 200 or greater with a grade of C- or better.
-
CS 251 Programming Languages: Design and Implementation 6 credits
What makes a programming language like “Python” or like “Java”? This course will look past superficial properties (like indentation) and into the soul of programming languages. We will explore a variety of topics in programming language construction and design: syntax and semantics, mechanisms for parameter passing, typing, scoping, and control structures. Students will expand their programming experience to include other programming paradigms, including functional languages like Scheme and ML.
- Fall 2025, Winter 2026
- FSR, Formal or Statistical Reasoning
-
Student has completed any one 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.
-
CS 251.01 Fall 2025
- Faculty: Anna Meyer 🏫 👤
- Size:28
- M, WHulings 316 1:50pm-3:00pm
- FHulings 316 2:20pm-3:20pm
-
17 seats held for CS Match until the day after rising sophomore (only) priority registration.
-
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.
Not open to students who have previously taken CS 320* (*=Junior Seminar).
X seats held for CS Match until the day after X priority registration.
- Winter 2026
- FSR, Formal or Statistical Reasoning
-
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 200 Requisite Equivalency AND CS 202 or MATH 236 with a grade of C- or better or received a Carleton Computer Science 202 Requisite Equivalency or received a Carleton Math 236 Requisite Equivalency. MATH 236 will be accepted in lieu of CS 202.
-
CS 320.01 Winter 2026
- Faculty: Anna Meyer 🏫 👤
- Size:28
- M, WWeitz Center 233 9:50am-11:00am
- FWeitz Center 233 9:40am-10:40am
-
28 seats held for CS Match until the day after Senior priority registration.
-
CS 394 Directed Research in Computer Science 1 – 6 credits
Students work on a research project related to a faculty member's research interests, and directed by that faculty member. Student activities vary according to the field and stage of the project. The long-run goal of these projects normally includes dissemination to a scholarly community beyond Carleton. The faculty member will meet regularly with the student and actively direct the work of the student, who will submit an end-of-term product, typically a paper or presentation.
Register for this course by submitting the Directed Research form which requires approval from the project faculty supervisor and your adviser.
- Spring 2026, Fall 2026, Winter 2027, Spring 2027
- No Exploration