Day 1: Foundations of Machine Learning and Optimization

Session 1: Introduction to Machine Learning and Optimization

• Key concepts and benefits of integration
• Overview of ML algorithms and optimization techniques

Session 2: Data Preparation for ML Models

• Cleaning, transforming, and engineering features
• Preparing a dataset for predictive modeling

Session 3: Building Predictive Models

• Training and evaluating regression and classification models
• Hands-on lab: Building a predictive model using Scikit-learn

Day 2: Optimization Techniques and Tools

Session 1: Linear and Nonlinear Optimization

• Formulating optimization problems and solving with Gurobi
• Implementing a linear programming problem in Python

Session 2: Integer Programming and Constraints

• Advanced techniques for constrained optimization
• Practical demonstration: Solving integer programming problems

Session 3: Sensitivity Analysis in Optimization

• Techniques for interpreting and refining solutions
• Performing sensitivity analysis on optimization results

Day 3: Integrating ML and Optimization

Session 1: Embedding ML Predictions into Optimization Models

• Using ML outputs as inputs for optimization workflows
• Combining ML predictions with an optimization problem

Session 2: Reinforcement Learning and Optimization

• Introduction to RL for decision-making and optimization
• Practical demonstration: Applying RL techniques to an optimization task

Session 3: Real-World Applications of ML-Optimization Integration

• Case studies in supply chain, energy, and healthcare
• Group activity: Designing an integrated solution for a business problem

Day 4: Advanced Techniques and Tools

Session 1: Neural Networks and Deep Learning in Optimization

• Using deep learning for predictive insights and optimization inputs
• Building a neural network model and integrating it into an optimization workflow

Session 2: Multi-Objective Optimization

• Techniques for balancing trade-offs between competing objectives
• Practical demonstration: Solving a multi-objective optimization problem

Session 3: Scaling and Automating Integrated Workflows

• Automation tools and cloud platforms for ML-optimization pipelines
• Deploying an integrated solution on a cloud platform

Day 5: Deployment and Future Trends

Session 1: Deploying Integrated Solutions

• Strategies for deploying ML and optimization models in production
• Implementing a deployment workflow with Python APIs

Session 2: Monitoring and Improving Deployed Models

• Setting up monitoring for integrated workflows
• Practical demonstration: Enhancing a deployed solution based on feedback

Session 3: Innovations and Future Trends

• Emerging tools and methods in ML and optimization integration
• Discussion: Adapting to future challenges and advancements