Optimization Concepts for Data Science and Artificial Intelligence
This course focuses on linear, nonlinear, and mixed integer linear optimization concepts in SAS Viya. Students learn how to formulate optimization problems and how to make their formulations efficient by using index sets and arrays. The demonstrations in the course include examples of diet formulation and portfolio optimization. Learn the OPTMODEL procedure and open source tools to formulate and solve optimization problems.
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Máme dostatečnou flexibilitu, takže vybírat můžete jak prezenční termíny, tak online kurzy.
Zkuste živý kurz virtuálněTarget group
Those who want to develop the optimization foundation necessary to work as a data scientist, especially those with a strong background in applied mathematics
Course structure
Introduction to Mathematical Optimization
- Introduction.
- A simple example.
- The OPTMODEL procedure.
Linear Programming
- Introduction to linear programming.
- Formulating and solving linear programming problems using the OPTMODEL procedure.
- Using index sets and arrays in the OPTMODEL procedure.
- Dual values and reduced costs in the simplex method (self-study).
- Reading and writing data in the OPTMODEL procedure.
Nonlinear Programming
- Introduction to nonlinear programming.
- Solving nonlinear programming problems using the OPTMODEL procedure.
Integer and Mixed Integer Linear Programming
- Introduction to integer and mixed integer linear programming.
- Solving integer and mixed integer linear programming problems using PROC OPTMODEL.
Open Source Interactivity
- SAS Viya and open source integration
- SAS Viya Python APIs.
Prerequisites
Before enrolling in this course, you should be comfortable with data manipulation using basic SAS tools. You can gain this course-specific knowledge in data manipulation by completing the SAS Programming 1: Essentials course. Some knowledge of linear programming concepts and matrix algebra is helpful but is not required.
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