WEBINAR / EVENT
Using Trained Machine Learning Predictors in Gurobi
Webinar: Using Trained Machine Learning Predictors in Gurobi.
January 25 | February 2 | February 7 2023
WEBINAR / EVENT
Using Trained Machine Learning Predictors in Gurobi
Webinar: Using Trained Machine Learning Predictors in Gurobi.
January 25 | February 2 | February 7 2023
WEBINAR / EVENT
Using Trained Machine Learning Predictors in Gurobi
Webinar: Using Trained Machine Learning Predictors in Gurobi.
January 25 | February 2 | February 7 2023
Event Recap
Machine learning has become a prevalent tool to provide predictive models in many applications, in this webinar relationships between variables of an optimization model in Gurobi will be discussed.
In recent years, machine learning has become a prevalent tool to provide predictive models in many applications. In this talk, we are interested in using such predictors to model relationships between variables of an optimization model in Gurobi. For example, a regression model may predict the demand of certain products as a function of their prices and marketing budgets among other features. We are interested in being able to build optimization models that embed the regression so that the inputs of the regression are decision variables, and the predicted demand can be satisfied.
We propose a python package that aims at making it easy to insert regression models trained by popular frameworks (e.g., scikit-learn, Keras, PyTorch) into a Gurobi model. The regression model may be a linear or logistic regression, a neural network, or based on decision trees.
Presented Materials:
Download the presentation, here.
Event Recap
Machine learning has become a prevalent tool to provide predictive models in many applications, in this webinar relationships between variables of an optimization model in Gurobi will be discussed.
In recent years, machine learning has become a prevalent tool to provide predictive models in many applications. In this talk, we are interested in using such predictors to model relationships between variables of an optimization model in Gurobi. For example, a regression model may predict the demand of certain products as a function of their prices and marketing budgets among other features. We are interested in being able to build optimization models that embed the regression so that the inputs of the regression are decision variables, and the predicted demand can be satisfied.
We propose a python package that aims at making it easy to insert regression models trained by popular frameworks (e.g., scikit-learn, Keras, PyTorch) into a Gurobi model. The regression model may be a linear or logistic regression, a neural network, or based on decision trees.
Presented Materials:
Download the presentation, here.
Event Recap
Machine learning has become a prevalent tool to provide predictive models in many applications, in this webinar relationships between variables of an optimization model in Gurobi will be discussed.
In recent years, machine learning has become a prevalent tool to provide predictive models in many applications. In this talk, we are interested in using such predictors to model relationships between variables of an optimization model in Gurobi. For example, a regression model may predict the demand of certain products as a function of their prices and marketing budgets among other features. We are interested in being able to build optimization models that embed the regression so that the inputs of the regression are decision variables, and the predicted demand can be satisfied.
We propose a python package that aims at making it easy to insert regression models trained by popular frameworks (e.g., scikit-learn, Keras, PyTorch) into a Gurobi model. The regression model may be a linear or logistic regression, a neural network, or based on decision trees.
Presented Materials:
Download the presentation, here.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Pierre Bonami
Principal Developer
Pierre Bonami holds a Ph.D. in Operations Research and Computer Science from University Paris 6. Prior to joining Gurobi he was one of the lead developers for CPLEX (2013-2020). Prior to that, he was a researcher for CNRS in Marseille University. He was also a postdoctoral fellow at Carnegie Mellon University and at IBM Research where he developed the open-source solver Bonmin. Pierre Bonami authored or co-authored more than 20 publications in top journals and conferences in the field of Mathematical Optimization. He is particularly well known for his work on Mixed-Integer Nonlinear Optimization and cutting planes for Mixed Integer Optimization.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Pierre Bonami
Principal Developer
Pierre Bonami holds a Ph.D. in Operations Research and Computer Science from University Paris 6. Prior to joining Gurobi he was one of the lead developers for CPLEX (2013-2020). Prior to that, he was a researcher for CNRS in Marseille University. He was also a postdoctoral fellow at Carnegie Mellon University and at IBM Research where he developed the open-source solver Bonmin. Pierre Bonami authored or co-authored more than 20 publications in top journals and conferences in the field of Mathematical Optimization. He is particularly well known for his work on Mixed-Integer Nonlinear Optimization and cutting planes for Mixed Integer Optimization.
Speakers
Meet Your Expert Speaker
Learn from the best in the industry, bringing years of experience and groundbreaking insights to the forefront of AI personalization.
Principal Developer
Pierre Bonami
Pierre Bonami holds a Ph.D. in Operations Research and Computer Science from University Paris 6. Prior to joining Gurobi he was one of the lead developers for CPLEX (2013-2020). Prior to that, he was a researcher for CNRS in Marseille University. He was also a postdoctoral fellow at Carnegie Mellon University and at IBM Research where he developed the open-source solver Bonmin. Pierre Bonami authored or co-authored more than 20 publications in top journals and conferences in the field of Mathematical Optimization. He is particularly well known for his work on Mixed-Integer Nonlinear Optimization and cutting planes for Mixed Integer Optimization.
