Training Machines to Learn the Way Humans Do - An Alternative to Backpropagation

Explore a lecture on alternative machine learning approaches that mimic human learning processes, focusing on local learning and dendrigated networks as alternatives to backpropagation. Delve into the biological basis of learning in animals, artificial neural networks, and the concept of gating inputs. Examine the cerebellum's role in learning, particularly Purkinje cells, and review computational experiments demonstrating the effectiveness of these approaches. Investigate applications in tasks such as the vestibulo-ocular reflex, predicting chaotic signals, and learning nutrition boundaries. Gain insights into the advantages of these biologically-inspired methods, including their ability to remember old tasks and train on multiple tasks simultaneously.

Introduction
How do animals learn
Machine learning
Artificial neural network
One layer networks
Multilayer networks
Local Learning
Dendritigated Networks
Gating
Inputs
Cat Theory
Local Learning Recap
Dendodegated Networks
Biologically Possible
Desirable Features
Cerebellum
Purkinje Cells
Experiments
Computational Experiments
Vestibulo ocular reflex
Cerebellum prediction
Learning chaotic signals
Plotting weights
Remembering old tasks
Training on multiple tasks
Hyperparameters
Example Task
Nutrition Boundaries
Learning
Directions
Conclusion
Interview