ML Basics for Manufacturing & Production Applications

This course is designed to provide a comprehensive foundation in the fundamental concepts and techniques of AI learning, specifically tailored for applications in Production Control and Operations (P&OC). Participants will gain a deep understanding of the underlying principles and algorithms that power modern AI systems, and learn how to apply these concepts to solve real-world problems in production scheduling, capacity planning, inventory control, and workflow optimization.

• Understand the fundamental principles and techniques of AI learning and their relevance to Production Control and Operations

• Apply supervised, unsupervised, and reinforcement learning algorithms to solve P&OC problems

• Develop a strong intuition for model selection, hyperparameter tuning, and performance evaluation in the context of P&OC

• Implement and deploy AI learning models using industry-standard tools and frameworks for production control and operations

• Communicate the results and insights obtained from AI learning models to both technical and non-technical stakeholders in P&OC

1. Introduction to AI Learning in P&OC

• Overview of AI learning and its applications in Production Control and Operations

• Types of learning: supervised, unsupervised, and reinforcement learning

• The AI learning process in P&OC: data preparation, model selection, training, evaluation, and deployment

• Hands-on exercises: Setting up the development environment and working with P&OC datasets

2. Supervised Learning for P&OC

• Overview of supervised learning and its applications in production control and operations

• Algorithms for classification and regression (e.g., logistic regression, decision trees, support vector machines)

• Model selection, hyperparameter tuning, and cross-validation techniques for P&OC problems

• Hands-on exercises: Implementing supervised learning algorithms for production scheduling and quality control

3. Unsupervised Learning in P&OC

• Overview of unsupervised learning and its applications in production control and operations

• Algorithms for clustering, dimensionality reduction, and anomaly detection (e.g., k-means, PCA, autoencoders)

• Techniques for data visualization and interpretation in P&OC

• Hands-on exercises: Applying unsupervised learning algorithms for workflow optimization and anomaly detection in production processes

4. Reinforcement Learning for P&OC

• Overview of reinforcement learning and its applications in production control and operations

• Markov Decision Processes (MDPs) and the Bellman equation in the context of P&OC

• Algorithms for value-based and policy-based reinforcement learning (e.g., Q-learning, SARSA, policy gradients)

• Hands-on exercises: Implementing reinforcement learning algorithms for inventory control and capacity planning

5. Advanced Topics and Applications in P&OC

• Deep learning architectures for AI learning in production control and operations (e.g., convolutional neural networks, recurrent neural networks)

• Transfer learning and domain adaptation techniques for P&OC data

• Real-world case studies and applications of AI learning in Production Control and Operations

• Hands-on exercises: Developing an end-to-end AI learning pipeline for a P&OC problem

• Strong understanding of mathematics, including linear algebra, calculus, and probability theory

• Proficiency in programming with Python or R

• Familiarity with basic machine learning concepts and algorithms

• Knowledge of production control and operations management principles