What is Machine Learning?
"Science of getting computers to act without being explicitly programmed" - Andrew Ng, Standford
Regular Programming:
Input + Rules => Output
Machine Learning:
Input + Output => Rules
Difference to AI and Deep Learning
https://blogs.nvidia.com/blog/2016/07/29/whats-difference-artificial-intelligence-machine-learning-deep-learning-ai/
When to use Machine Learning?
- a pattern exists
- cannot be solved mathematically
- data is available
Types of Machine Learning
- Supervised Learning
- input/output pairs
- classification
- Unsupervised Learning
- only input
- finding clusters/categories
- Reinforcement Learning
- input + grade of output
- finding ideal behaviour for context
Perceptron Learning Algorithm
- Supervised ML
- Classification
- Data needs to be linearly separable
Neural Networks
- Multiple perceptrons
- Output from one (hidden) layer -> input for next
Introduction to Machine Learning
Simon Reinsperger | piedcode.com | @simon_rsp
https://github.com/abisz/talk-ml-introduction
