Search Results
Your search for courses · during 24FA, 25WI, 25SP · taught by tfinzell · returned 3 results
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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.
- Fall 2024, Winter 2025, Spring 2025
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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NOT open to students who have completed any of the following course(s): CS 201 or greater with a grade of C- or better.
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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.
- Spring 2025
- FSR, Formal or Statistical Reasoning
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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.
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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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CS 364 Computational Modeling and Simulation of Natural Systems 6 credits
Computational models have become a fundamental part of how we make sense of the world, doing everything from economic forecasting to simulating the birth of the universe. But we need to understand how to use models effectively. In this class we’ll explore computational models used across many disciplines, including: agent-based models to prevent forest fires, compartmental models to protect endangered species, N-body models to track the spread of germs from a sneeze, and more. We’ll learn about what problems are (and are not) suited for computational modeling and engage with extensive datasets to evaluate and refine models for practical use.
- Fall 2024
- FSR, Formal or Statistical Reasoning QRE, Quantitative Reasoning
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Student has completed any of the following course(s): CS 200 – Data Structures with Problem Solving or CS 201 – Data Structures with a grade of C- or better or equivalent.