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Machine Learning Fundamentals with Applications in Photonics

A tutorial that discusses the fundamentals of AI and ML, with specific applications in the area of optics and photonics. Artificial intelligence and machine learning provide us with a fundamentally new toolkit for solving a variety of complex problems. The tutorial covers the following: - What is the relationship between artificial intelligence, machine learning and deep learning? - Supervised learning, unsupervised learning and reinforcement learning approaches. - Deep learning and the types of problems that are best suited for deep neural networks. - Software and hardware requirements for ML development. - Applications of classical ML algorithms and deep learning in photonics. Artificial intelligence, machine learning, and deep learning 2:00 Machine learning categorization 4:00 Inference and generalization 7:52 Model evaluation 9:54 Software frameworks and hardware requirements for ML development 16:42 Support vector machines (SVMs) 20:41 SVM for prediction of optical chip performance 23:42 Ensemble learning 28:13 Decision trees and random forests 29:41 Random forest assisted vector displacement sensor 31:51 Artificial neural networks (ANNs) and deep learning 32:23 Gradient descent 35:38 Gradient optimization in nanophotonic inverse design 36:44 Backpropagation training algorithm 38:08 Deep learning for photonic chip design optimizations 40:36 Data augmentation 45:40 Transfer learning 46:58 Transfer learning in multilayered structures 47:52 Transformer network 49:49 GPT for inverse design of optical thin film structures 54:00 Reinforcement learning 55:05 Multiagent reinforcement learning in underwater optical wireless sensor networks 55:32 Visual summary 58:26 https://www.enablence.com