Semi-Supervised & Transfer Learning for Quality Management 

This course equips quality professionals with powerful AI techniques to overcome the challenge of limited labeled data in quality control tasks. You'll explore the fundamentals of semi-supervised learning and transfer learning, delve into their potential applications for various quality control scenarios, and gain hands-on experience with implementing them to unlock valuable insights from your data. This empowers you to improve quality control processes even with smaller datasets, ultimately achieving better results with fewer resources. 

• Explain the limitations of traditional supervised learning when dealing with limited labeled data in quality control tasks.

• Understand the core principles of semi-supervised learning and transfer learning, and how they leverage different data sets to improve model performance.

• Decipher key algorithms for each technique:

  • Semi-supervised learning: self-training, consistency regularization

  • Transfer learning: fine-tuning pre-trained models

• Identify potential applications of both techniques in quality control tasks, such as:

  • Semi-supervised learning:

    • Enhancing defect detection models with unlabeled product images.

    • Improving sentiment analysis of customer reviews with unlabeled data.

    • Training anomaly detection models for sensor data with limited labeled anomalies.

  • Transfer learning:

    • Adapting pre-trained image recognition models (e.g., ResNet) for product quality inspection.

    • Fine-tuning pre-trained NLP models (e.g., BERT) to analyze quality-related text data.

• Utilize a user-friendly platform or library (e.g., PyTorch) to implement chosen techniques for a quality control task using a limited labeled dataset.

• Evaluate the strengths and limitations of both techniques compared to traditional supervised learning for quality control applications with limited data.

• Discuss the ethical considerations surrounding the use of unlabeled data and pre-trained models, such as potential biases and data quality issues.

• Explore emerging trends in semi-supervised learning and transfer learning research and their impact on future quality control practices, including active learning for data labeling and integration with other AI techniques.

1. Semi-Supervised Learning:
   
   

• Supervised learning with limited labeled data and introducing semi-supervised learning and transfer learning as effective alternatives.

• Semi-Supervised Learning Fundamentals: Demystifying core principles of semi-supervised learning, exploring different algorithms (self-training, consistency regularization), and understanding their advantages for leveraging unlabeled data.

• Case Study 1: Enhancing Defect Detection with Semi-Supervised Learning: Analyzing a real-world scenario of using a semi-supervised learning approach to improve a defect detection model for product images with a limited set of labeled defect examples.

• Quality control challenges within your company that involve limited labeled data and discussing the potential application of semi-supervised learning techniques.

• Hands-on Session 1: Implementing Semi-Supervised Learning: Utilizing a user-friendly platform or library (e.g., PyTorch) to implement a chosen semi-supervised learning algorithm on a quality control-related dataset with limited labeled data.

2. Transfer Learning:
   
   

• Transfer Learning: Pre-Trained Models for Faster AI Development: Introducing the concept of transfer learning and its effectiveness in leveraging pre-trained deep learning models for quality control tasks.

• Exploring Pre-Trained Deep Learning Architectures: Delving into popular pre-trained models for image recognition (e.g., ResNet) and natural language processing (e.g., BERT), and understanding their potential adaptation to quality control applications.

• Case Study 2: Fine-Tuning a Pre-Trained Model for Customer Review Analysis: Analyzing a real-world scenario of using transfer learning to fine-tune a pre-trained NLP model for sentiment analysis of customer reviews to identify quality-related concerns.

• Hands-on Session 2: Implementing Transfer Learning: Utilizing the chosen platform or library (e.g., PyTorch) to fine-tune a pre-trained deep learning model for a quality control task using a limited labeled dataset.

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

• Proficiency in programming with Python and deep learning frameworks (e.g., TensorFlow, PyTorch)

• Familiarity with supervised learning concepts and techniques (e.g., classification, regression, neural networks)

• Knowledge of unsupervised learning methods (e.g., clustering, dimensionality reduction) is beneficial but not required