Introduction

Optimization lies at the heart of data science, enabling the efficient solution of complex problems in machine learning, operations research, and decision-making. This 5-day intensive training course is designed to provide participants with a deep understanding of optimization techniques, from foundational algorithms to advanced methods. Participants will learn how to apply optimization to real-world problems, improve model performance, and make data-driven decisions. The course also addresses ethical considerations and future trends, ensuring participants are prepared to tackle modern challenges in data science.

Objectives

By the end of this course, participants will:

1. Understand the fundamentals of optimization, including problem formulation, convexity, and gradient-based methods.

2. Gain proficiency in using optimization libraries such as SciPy, CVXPY, and Pyomo.

3. Learn how to apply optimization techniques to machine learning, including hyperparameter tuning and model training.

4. Explore advanced methods such as stochastic optimization, metaheuristics, and constrained optimization.

5. Apply optimization techniques to real-world problems in logistics, finance, healthcare, and more.

6. Understand ethical considerations and future trends in optimization, including AI-driven optimization and explainable decision-making.

Who Should Attend?

This course is ideal for:

• Data scientists and machine learning engineers looking to enhance their optimization skills.

• Operations researchers and analysts seeking to solve complex decision-making problems.

• Software developers and engineers interested in integrating optimization into applications.

• Researchers and academics exploring advanced optimization techniques.

• Professionals in logistics, finance, healthcare, and other industries where optimization is critical.

• AI enthusiasts and practitioners preparing for future challenges in data-driven optimization.

Course Outline

Day 1: Foundations of Optimization

• Morning Session:

  • Introduction to Optimization: Problem Formulation, Objectives, and Constraints

  • Types of Optimization Problems: Linear, Nonlinear, Convex, and Non-Convex

  • Hands-on Lab: Solving Linear Programming Problems with SciPy

• Afternoon Session:

  • Convexity and Duality: Key Concepts and Applications

  • Gradient-Based Methods: Gradient Descent and Newton’s Method

  • Case Study: Portfolio Optimization in Finance

Day 2: Optimization in Machine Learning

• Morning Session:

  • Optimization for Model Training: Stochastic Gradient Descent (SGD) and Variants

  • Hands-on Lab: Implementing SGD for Linear Regression

  • Hyperparameter Tuning: Grid Search, Random Search, and Bayesian Optimization

• Afternoon Session:

  • Hands-on Lab: Hyperparameter Optimization with Optuna

  • Case Study: Optimizing a Machine Learning Pipeline

  • Challenges in Optimization: Local Minima, Saddle Points, and Convergence

Day 3: Advanced Optimization Techniques

• Morning Session:

  • Constrained Optimization: Lagrange Multipliers and Penalty Methods

  • Hands-on Lab: Solving Constrained Problems with CVXPY

  • Integer and Mixed-Integer Programming: Branch-and-Bound and Cutting Planes

• Afternoon Session:

  • Metaheuristics: Genetic Algorithms, Simulated Annealing, and Particle Swarm Optimization

  • Hands-on Lab: Implementing a Genetic Algorithm for Feature Selection

  • Case Study: Supply Chain Optimization Using Metaheuristics

Day 4: Stochastic and Large-Scale Optimization

• Morning Session:

  • Stochastic Optimization: Stochastic Gradient Descent (SGD) and Mini-Batch Methods

  • Hands-on Lab: Implementing SGD for Large-Scale Data

  • Distributed Optimization: Parallel and Federated Learning

• Afternoon Session:

  • Hands-on Lab: Distributed Optimization with Dask

  • Case Study: Optimizing Large-Scale Machine Learning Models

  • Ethical Considerations: Fairness and Bias in Optimization

Day 5: Real-World Applications and Capstone Project

• Morning Session:

  • Deploying Optimization Models: Tools and Best Practices

  • Model Interpretability and Explainable Optimization

  • Future Trends: AI-Driven Optimization and Quantum Computing

• Afternoon Session:

  • Capstone Project: End-to-End Optimization Solution for a Real-World Problem

  • Project Presentations and Feedback

  • Course Wrap-up: Key Takeaways, Resources for Further Learning, and Certification

Key Features of the Course

• Hands-on labs using modern tools like SciPy, CVXPY, Pyomo, Optuna, and Dask.

• Real-world case studies and industry-relevant applications.

• Focus on ethical AI, model interpretability, and future-proofing skills.

• Access to course materials, code repositories, and a community forum for ongoing learning.

Preparing for Future Challenges

This course is designed to not only address current industry needs but also prepare participants for emerging trends and challenges in optimization. By focusing on ethical AI, explainability, and advanced techniques, attendees will be equipped to lead innovation and adapt to the rapidly evolving landscape of data-driven optimization.