Machine Learning - The Bare Math Behind Libraries

Dive into the mathematical foundations of machine learning in this 53-minute conference talk from NDC Conferences. Explore the history and basic techniques of supervised and unsupervised learning. Gain a deeper understanding of gradient descent algorithms, linear regression, and neural network training. Discover Hebb's learning and learning with concurrency methods in unsupervised learning. Learn to select appropriate parameters and network types for your projects using Octave examples. Equip yourself with the knowledge to confidently implement machine learning in your work and prepare for more advanced deep learning techniques.

Intro
Machine Learning
Biological Neuron
Activation function
What defines a superhero?
Gradient descent
NN - backpropagation step
Unsupervised learning
Hebbian learning weaknesses
Learning with concurency - weaknesses
Common weaknesses of artificial neuron systems
Bibliography