ML Basics for Quality Management Applications

This course equips quality professionals with the fundamental knowledge and skills of Artificial Intelligence (AI) to enhance their quality management practices. You'll explore core AI concepts, understand how AI can be applied to various quality management tasks, and gain hands-on experience with practical tools and techniques. This empowers you to leverage AI to improve data-driven decision making, automate repetitive tasks, and gain deeper insights into product and process quality. 

• Explain the fundamental concepts of Artificial Intelligence (AI), including machine learning, deep learning, and natural language processing.

• Identify the potential applications of AI in various quality management tasks, such as defect detection, process optimization, and customer feedback analysis.

• Understand the key considerations for implementing AI solutions within a quality management framework, including data quality, model selection, and bias mitigation.

• Explore popular AI tools and techniques relevant to quality management, including data visualization, anomaly detection, and sentiment analysis.

• Gain hands-on experience using a user-friendly AI platform or library (e.g., Google Cloud AI Platform, TensorFlow Lite) to apply AI techniques to real-world quality management scenarios.

• Evaluate the impact of AI on quality control processes and identify potential challenges and ethical considerations.

• Develop a high-level plan for integrating AI into your quality management workflow, considering specific use cases and resource constraints.

1. Unveiling the Power of AI for Quality Management:

• The Quality Challenge in the Age of Data: Highlighting the increasing complexity of quality management in large and medium-sized companies and the growing potential of AI to address these challenges.

• Demystifying AI: Introducing core AI concepts like machine learning, deep learning, and natural language processing, focusing on their potential applications in quality control tasks.

• Case Study 1: Analyzing a real-world scenario of using AI for automated visual inspection of manufactured goods, showcasing the benefits and considerations for implementation.

• Interactive Workshop: Exploring different AI applications in quality management through interactive demonstrations and discussions (e.g., anomaly detection in sensor data, sentiment analysis of customer reviews).

• Guest Speaker Session: Inviting a quality management professional with experience in AI implementation to discuss success stories and practical considerations for integrating AI in quality management workflows.

2. Putting AI into Action for Quality Management:

• Understanding AI Tools & Techniques: Introducing popular AI tools and techniques relevant to quality control, including data visualization tools, anomaly detection algorithms, and sentiment analysis methods.

• Hands-on Session 1: Utilizing a user-friendly AI platform or library (e.g., Google Cloud AI Platform, TensorFlow Lite) to analyze real-world quality control data (e.g., sensor readings, customer feedback) and apply chosen AI techniques for anomaly detection or sentiment analysis.

• Hands-on Session 2: Building a simple AI-powered quality control model using a pre-trained model or a user-friendly platform to address a specific quality management task (e.g., product classification based on image data).

• Ethical Considerations & Responsible AI: Discussing potential biases in AI systems and strategies for mitigating bias in quality management applications.

• The Future of AI in Quality Management: Exploring emerging trends in AI and their potential impact on future quality control processes.

• Course Wrap-up & Group Project Presentations: Teams develop a high-level plan for integrating an AI solution into a specific quality management process within their company. The plan should consider the chosen AI technique, potential challenges, ethical considerations, and expected benefits. 

• Basic understanding of mathematics, including calculus and linear algebra

• Familiarity with programming concepts and a language such as Python

• Knowledge of basic machine learning concepts and algorithms