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Class number:
2935
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Title: Statistical Learning |
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Department: Mathematics |
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Career: Undergraduate |
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Component: Lecture |
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Session: Regular |
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Instructor's Permission Required: No |
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Grading Basis: Regular |
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Units: 1.00 |
| Enrollment limited to 24 |
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Current enrollment: 8 |
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Available seats: 16 |
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Start date: Tuesday, January 20, 2026 |
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End date: Friday, May 8, 2026 |
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Mode of Instruction: In Person |
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Schedule: MWF: 10:00AM-10:50AM, LSC - 131 |
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Instructor(s): Churchill, Victor |
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Prerequisite(s): Prerequisite: C- or better in Mathematics 212 and Mathematics 228 or Mathematics 229, or permission of instructor. |
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Distribution Requirement: Meets Numerical & Symbolic Reasoning Requirement |
Course Description:
This course provides a comprehensive introduction to foundational and advanced techniques in estimation and modeling from a mathematical standpoint. Key topics include maximum likelihood estimation, Bayesian inference, Markov chain Monte Carlo (MCMC) sampling, linear and regularized regression, as well as nonlinear approaches such as neural networks. Additional topics may cover dimension reduction, dealing with noisy and limited data, data visualization, optimization, and approximation theorems. Through programming-based assignments in MATLAB or Python, students will apply theoretical concepts to real-world problems, gaining hands-on experience in data analysis and model building. |