Lecture
This lecture covers bio-inspired learning techniques such as artificial neural networks and genetic algorithms, which mimic biological processes to simplify models. Topics include neural network structure, activation functions, training methods like backpropagation, and the practical applications of deep learning. The instructor explains how genetic algorithms optimize solutions through mutation and combination, and how learning by discovery can generate new knowledge. The lecture also explores the challenges and unpredictability of deep learning, the importance of diversity in genetic algorithms, and the use of meta-heuristics to modify heuristics for better performance.