Machine Learning - The Bare Math Behind Libraries

Dive into the mathematical foundations of machine learning in this 44-minute Devoxx conference talk. Explore the history and basic techniques of supervised and unsupervised learning, gaining intuition on how machine learning works. Understand gradient descent algorithms through simple linear regression, and see how this applies to neural network training. Learn about Hebb's learning and concurrency-based algorithms in unsupervised learning. Use Octave for practical examples, but apply the concepts to any preferred technology. Gain confidence in selecting network parameters and types, preparing you for more advanced deep learning methods. Benefit from the presenters' expertise in software engineering, artificial intelligence, and natural language processing as they demystify the math behind popular machine learning libraries.

Introduction
Definitions
In Practice
Gradient Descent
Neural Network
Unsupervised Learning
Winner Takes All
Winner Takes Most
SelfOrganizing Map
Solution Space
Conclusion
Outro