The lecture series on "Introduction to Neural Networks" by F. James, held from January 31 to February 4, 1994, focuses on solving real problems using multi-layer feed-forward networks. The series covers the general theory of inverse problems and computational complexity in neural networks, aiming to understand the solvability of problems and the necessary network architecture. Key topics include: 1. **Introduction and Overview**: Basic concepts and applications of Artificial Neural Networks (ANNs). 2. **Feed-forward Network as an Inverse Problem**: Discussion on computational complexity and network training. 3. **Physics Applications**: Practical applications of neural networks in physics, such as pattern recognition and jet identification. The lecture also highlights the importance of ANNs in high-energy physics, emphasizing their ability to handle complex pattern recognition tasks. The series concludes with a discussion on the challenges and future directions in neural network research and applications.The lecture series on "Introduction to Neural Networks" by F. James, held from January 31 to February 4, 1994, focuses on solving real problems using multi-layer feed-forward networks. The series covers the general theory of inverse problems and computational complexity in neural networks, aiming to understand the solvability of problems and the necessary network architecture. Key topics include: 1. **Introduction and Overview**: Basic concepts and applications of Artificial Neural Networks (ANNs). 2. **Feed-forward Network as an Inverse Problem**: Discussion on computational complexity and network training. 3. **Physics Applications**: Practical applications of neural networks in physics, such as pattern recognition and jet identification. The lecture also highlights the importance of ANNs in high-energy physics, emphasizing their ability to handle complex pattern recognition tasks. The series concludes with a discussion on the challenges and future directions in neural network research and applications.