Optimization Methods In Data Science
The IEMS 351 Optimization Methods In Data Science course introduces students to mathematical nonlinear optimization and applications in data science. In this course students will learn about the Python programming language and focus on writing implementations of algorithms. In this course students will study the fundamental algorithms and theoretical underpinnings for nonlinear optimization with a focus on support vector machines, nonlinear regression, deep neural networks, and logistic regression.
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Check out the list of following courses offered by our experienced tutors -
- IEMS 200 Foundations of Data Science
- IEMS 304: Statistical Learning for Data Analysis
- IEMS 313: Foundations of Optimization
- IEMS 373: Intro to Financial Engineering
- IEMS 450-1: Mathematical Optimization I
- IEMS 488: Economics of Decision Analysis
- IEMS 401: Applied Mathematical Statistics
- IEMS 381: Supply-Chain Modeling and Analysis
- IEMS 307: Quality Improvement by Experimental Design